Eye-gesture control of computer systems via artificial intelligence

Nachaat Mohamed

doi:10.12688/f1000research.144962.3

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Research Article

Revised

Eye-gesture control of computer systems via artificial intelligence

[version 3; peer review: 1 approved, 2 approved with reservations]

Nachaat Mohamed

PUBLISHED 25 Feb 2025

Author details Author details

HLS, Rabdan Academy, Abu Dhabi, Abu Dhabi, United Arab Emirates

Nachaat Mohamed
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background

Artificial Intelligence (AI) offers transformative potential for human-computer interaction, particularly through eye-gesture recognition, enabling intuitive control for users and accessibility for individuals with physical impairments.

Methods

We developed an AI-driven eye-gesture recognition system using tools like OpenCV, MediaPipe, and PyAutoGUI to translate eye movements into commands. The system was trained on a dataset of 20,000 gestures from 100 diverse volunteers, representing various demographics, and tested under different conditions, including varying lighting and eyewear.

Results

The system achieved 99.63% accuracy in recognizing gestures, with slight reductions to 98.9% under reflective glasses. These results demonstrate its robustness and adaptability across scenarios, confirming its generalizability.

Conclusions

This system advances AI-driven interaction by enhancing accessibility and unlocking applications in critical fields like military and rescue operations. Future work will validate the system using publicly available datasets to further strengthen its impact and usability.

Keywords

Artificial Intelligence, Computers, Gestures, OpenCV, Python, Pyautogui

Corresponding author: Nachaat Mohamed

Competing interests: No competing interests were disclosed.

Grant information: This work was supported by Rabdan Academy.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2025 Mohamed N. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Mohamed N. Eye-gesture control of computer systems via artificial intelligence [version 3; peer review: 1 approved, 2 approved with reservations]. F1000Research 2025, 13:109 (https://doi.org/10.12688/f1000research.144962.3) First published: 19 Feb 2024, 13:109 (https://doi.org/10.12688/f1000research.144962.1) Latest published: 25 Feb 2025, 13:109 (https://doi.org/10.12688/f1000research.144962.3)

Revised Amendments from Version 2

In this revised version, we have made several key enhancements to improve the manuscript:
Introduction: Expanded to include objectives, motivations, and specific research questions guiding the study.
Literature Review: Updated to incorporate recent studies, including "FSTL-SA: Few-Shot Transfer Learning for Sentiment Analysis from Facial Expressions" by Meena et al. (2024).
Methodology:
Embedding Techniques: Provided detailed explanations of the embedding methods used.
Model Fine-Tuning: Added specifics on the fine-tuning process and experimental setups.
Comparative Analysis: Included a table comparing our work with recent studies, such as "Monkeypox Recognition and Prediction from Visuals Using Deep Transfer Learning-Based Neural Networks" by Meena et al. (2024).
Dataset Description: Introduced a subsection detailing the dataset's size and composition.
Challenges and Future Work: Discussed encountered challenges and proposed directions for future research.
These revisions aim to enhance clarity, comprehensiveness, and the overall quality of the manuscript.

See the author's detailed response to the review by Haodong Chen
See the author's detailed response to the review by Gaurav Meena
See the author's detailed response to the review by Zakariyya Abdullahi Bature

Introduction

Human-Computer Interaction (HCI) has evolved significantly from its inception, which featured punch cards and command line interfaces, to today’s sophisticated Graphical User Interfaces (GUIs) and Natural Language Processing (NLP) technologies. Despite these advancements, traditional input devices such as keyboards and mice have limitations, particularly for users with motor impairments.¹ Eye-tracking technologies, which interpret users’ intentions through ocular movement analysis, present a promising solution to these challenges.² However, realizing their full potential requires the integration of Artificial Intelligence (AI) to accurately interpret nuanced eye movements. This paper introduces an AI-enhanced system for computer control using eye gestures. By harnessing advanced computer vision and machine learning techniques, we translate users’ eye and facial gestures into precise computer commands.³^,⁴ Such eye-gesture systems not only promise more intuitive interactions but also offer ergonomic benefits, representing a departure from traditional input devices.⁵^,⁶ Their potential is particularly significant for individuals with disabilities, such as mobility challenges or spinal cord injuries, as they provide an alternative means of control.⁷ Furthermore, these systems are beneficial for professionals like surgeons or musicians who require hands-free computer interactions.⁸ The market is currently filled with eye-gesture systems that employ various technologies.⁹^,¹⁰ However, our AI-driven approach aims to set a new benchmark. Figure 1 shows the Comparison of Ease of Use between Traditional Input Devices and Eye-Tracking Technologies for Different User Group.

Figure 1. Comparison of Ease of Use between Traditional Input Devices and Eye-Tracking Technologies for Different User Group.

We posit that our methodologies could revolutionize HCI, fostering a more accessible and intuitive user experience.¹¹ Moreover, our research opens the door to innovative applications such as gesture-based weaponry systems.

In recent years, eye-gesture recognition has gained significant attention as a promising method for enhancing human-computer interaction. Despite advancements, existing systems often suffer from limitations such as low accuracy, high latency, and dependency on specialized hardware, which restrict their real-world applicability. Several studies have proposed gaze-based systems; however, these primarily focus on tracking eye movement direction rather than recognizing complex eye gestures. This leaves a substantial gap in creating robust, high-accuracy systems capable of performing detailed commands solely through eye gestures without external hardware dependencies.

The objective of this study is to bridge this gap by developing an AI-driven eye-gesture recognition system that offers high accuracy (99.63%), real-time performance, and easy integration using widely available tools like OpenCV and PyAutoGUI. Our motivation stems from the need to create a system that enhances accessibility for individuals with physical impairments while offering scalable applications in fields like healthcare, assistive technologies, and military systems. By addressing these gaps, we aim to provide a practical solution that outperforms existing systems in terms of accuracy, usability, and adaptability.

To address the identified gaps, this research seeks to answer the following key questions:

How can AI-based models be optimized to recognize complex eye gestures with high accuracy and real-time responsiveness?

What impact does dataset diversity have on the generalizability and robustness of the proposed system across different user groups?

How does the proposed system compare to state-of-the-art gaze-based and multi-modal interaction frameworks in terms of accuracy, hardware requirements, and real-world usability?

What challenges arise in developing and deploying an eye-gesture control system in diverse environmental conditions, and how can these be mitigated?

Problem statement

In the evolving landscape of Human-Computer Interaction (HCI), ensuring seamless and intuitive interactions is paramount, especially for users with physical impairments or specialized professional requirements.¹² While traditional input devices such as keyboards and mice have served a majority of users effectively, they present inherent limitations for certain cohorts. These limitations underscore the need for alternative interaction paradigms. Eye-gesture technologies have emerged as potential candidates to bridge this gap. However, existing eye-gesture systems, although varied in their technological foundations, often lack the sophistication required to interpret a wide array of user intentions accurately and responsively. The challenge lies in harnessing the full potential of eye-tracking technologies by integrating advanced Artificial Intelligence (AI) capabilities, ensuring precise interpretation of eye movements, and translating them into actionable computer commands. Addressing this challenge is imperative to create a universally accessible and efficient HCI platform, capable of catering to a diverse range of users and use-cases.

Background

Artificial Intelligence (AI) has evolved into a comprehensive domain, influencing a myriad of sectors. A compelling facet within this expansive realm is AI gestures: the mimicked non-verbal cues generated by AI systems, aimed at fostering human-like interactions. These gestures, characterized by actions such as waving, nodding, or pointing, enhance the depth of human-AI communication, drawing from advanced technologies like robotics, computer vision, and natural language processing.¹³^,¹⁴ The potency of AI gestures is amplified by leveraging the powerful programming language, Python. Its rich assortment of libraries, such as NumPy, Pandas, and scikit-learn, facilitates diverse functionalities crucial for AI and machine learning applications.¹⁵^,¹⁶ Central to AI gesture recognition is the library OpenCV (Open Source Computer Vision). Originating from Intel’s innovation and now under Itseez’s stewardship, OpenCV encompasses an extensive suite of over 2,500 computer vision and machine learning algorithms. Its capabilities span facial recognition, object detection, tracking, and more, finding application across industries like robotics, healthcare, security, and entertainment.¹⁷^,¹⁸ Enthusiasts and professionals can leverage OpenCV’s robust documentation, tutorials, and a wealth of external resources to harness its full potential.¹⁹

Motivations

In today’s rapidly digitizing world, the very essence of human-computer interaction is undergoing significant evolution.²⁰ As our reliance on digital systems amplifies, there’s a pressing need to make these interactions more intuitive, accessible, and versatile. The conventional modalities—keyboards, mice, touchscreens, while revolutionary in their own right, present inherent limitations.²¹ These limitations become especially pronounced when considering populations with specific needs or challenges, such as those with motor impairments.²² The quest for inclusivity in technology beckons innovations that can be seamlessly integrated into the lives of all individuals, irrespective of their physical capacities. Eye-gesture recognition emerges as a beacon of promise in this quest. The human eye, a marvel of nature, not only perceives the world but can also communicate intent, emotion, and directives. Harnessing this potential could redefine the paradigms of interaction, enabling users to convey commands or intentions to machines just by moving their eyes. Imagine a world where, with a mere glance, individuals can operate their devices, access information, or even control their home environments. The implications are transformative not just as a novel method of interaction but as a lifeline of autonomy for those who’ve traditionally been dependent on others for even the most basic digital tasks. Moreover, the contemporary technological landscape, enriched by the advancements in Artificial Intelligence (AI), presents an opportune moment for such innovations. AI, with its ability to learn, interpret, and predict, can elevate eye-gesture systems from being mere interpreters of movement to intelligent entities that understand context, nuance, and subtleties of human intent. Yet, for all its promise, the realm of eye-gesture recognition remains a burgeoning field with vast unexplored potentials. The convergence of AI and eye-tracking technologies could spawn a revolution, akin to the leaps we’ve witnessed with touch technologies and voice commands. It is this potential for transformative impact, the prospect of bridging gaps in accessibility, and the allure of uncharted technological frontiers that serves as the driving motivation behind our research.

Related work

Gesture recognition has its roots in early computer vision studies, with VPL Research being among the first to market a data glove as a gesture input device in the 1980s.¹^,²³ This pioneering work was expanded upon by Freeman and Roth, who used orientation histograms for hand gesture recognition, laying foundational methodologies for future research.²⁴ O’Hagan et al. documented another breakthrough in 1996 when they applied Hidden Markov Models (HMM) to hand gesture recognition, introducing statistical methods to the domain.²^,²⁵ The Microsoft Kinect, launched in 2010, was a game-changer for gesture-based HCI. Its depth camera and IR sensor allowed for full-body 3D motion capture, object recognition, and facial recognition, marking a significant step forward in home-based gesture recognition systems.²⁶ Meanwhile, the Leap Motion controller, a compact device capable of detecting hand and finger motions, allowed for fine-grained gesture recognition and was integrated into virtual reality setups to provide natural hand-based controls.³^,²⁷ From the algorithmic perspective, Random Decision Forests (RDF) played a crucial role in the success of Kinect’s skeletal tracking capabilities.²⁸ Deep Learning, specifically Convolutional Neural Networks (CNN), further revolutionized the field by enabling real-time hand and finger gesture recognition with unprecedented accuracy.²⁹ This development was pivotal in the success of systems such as Google’s Soli, a miniature radar system that recognizes intricate hand movements, epitomizing the potency of melding advanced hardware and sophisticated algorithms.⁴^,³⁰ In a seminal paper by Karam et al., gesture-based systems were explored as assistive technologies, illustrating how gesture recognition can be tailored to the unique needs and capabilities of users with disabilities.⁵^,³¹ Another notable work by Vogel and Balakrishnan explored the implications of using gestures in “public spaces”, highlighting the social aspects and challenges of gesture-based interfaces.³² In VR and AR, gesture control has been crucial in creating immersive experiences. Bowman et al.’s comprehensive survey of 3D user interfaces elaborated on the role of gestures in navigating virtual environments.⁶^,³³ Furthermore, research by Cauchard et al. highlighted the potential of drones being controlled by body gestures, showcasing the fusion of gesture recognition with emerging technologies.³⁴ While gesture recognition has come a long way, it isn’t without challenges. Wu et al. outlined the difficulties in recognizing gestures in cluttered backgrounds, especially in dynamic environments.³⁵ Moreover, a study by Nielsen et al. pointed out that while gestures can be intuitive, they can also be fatiguing, coining the term “Gorilla Arm Syndrome” to describe the fatigue resulting from extended use of gesture interfaces.⁷^,³⁶ The intersection of Gesture control technology and Artificial Intelligence (AI) has emerged as a pivotal axis in the realm of human-computer interaction, heralding unprecedented modalities through which humans engage with digital ecosystems. Historically, the rudimentary applications of this confluence were discernible in the use of hand gestures for smartphones or tablets, a domain that has since witnessed radical metamorphosis.¹⁴^,¹⁸^,²³^–²⁶^,²⁸^–³²^,³⁴^,³⁵^,³⁷^–⁵³ The contemporary landscape sees gesture control permeating environments as expansive as desktops, where intricate hand movements can seamlessly manage presentations or navigate through web interfaces.³⁸^,³⁹ At a granular level, the progression of gesture control traverses two salient trajectories: the deployment of specialized hardware and the adoption of software-centric solutions.⁵⁴ The former, entailing components such as dedicated motion sensors or depth-sensing cameras, while ensuring superior precision, often weighs heavily on financial metrics.⁴⁰ In stark contrast, software-oriented paradigms capitalize on standard cameras, superimposed with intricate AI algorithms to track and decipher gestures.⁴¹ While this approach champions cost-effectiveness, it sometimes grapples with challenges related to reliability and fidelity of gesture interpretation.⁵⁵ Notwithstanding these teething challenges, the inherent potential of gesture control, particularly when augmented by AI, promises to redraw the contours of human-machine interfaces, making them more intuitive and universally accessible. AI’s salience in this revolution is underpinned by its capacity to process and interpret human movements, a capability that metamorphoses mere physical gestures into coherent commands for devices.⁴²^,⁵⁶ Beyond mere gesture recognition, AI also serves as the lynchpin for virtual assistants such as Siri and Google Assistant, facilitating their control through voice and gesture symbiotically.⁴³^,⁴⁴ Virtual Reality (VR) and Augmented Reality (AR) platforms further underscore the transformative power of melding AI and gesture control. Real-time gesture interpretations in these platforms magnify user immersion, enabling an unprecedented interaction level with virtual realms.¹⁴^,¹⁸^,²³^–²⁶^,²⁸^–³²^,³⁴^,³⁵^,⁴⁴^–⁵⁴^,⁵⁶^,⁵⁷ On the hardware front, devices such as the Leap Motion controller and the Myo armband are exemplary testaments to the future of gesture control. These devices, empowered by AI, meticulously interpret intricate hand gestures and muscle movements, offering a plethora of command capabilities.⁴⁷^,⁵¹ AI-imbued gesture technology’s most heartening promise lies in its ability to democratize accessibility.⁴⁸^,⁵⁸ By transforming subtle human movements, ranging from the sweep of a hand to the blink of an eye, into actionable digital commands, the technology offers newfound autonomy to individuals facing mobility constraints.⁵⁶ The ripple effect of this technology is palpable in domains as diverse as gaming, entertainment, and the burgeoning field of smart home automation.⁴⁹ The gamut of applications suggests benefits that transcend mere accessibility, spanning intuitive interaction paradigms and conveniences across multifarious scenarios.⁵⁰^,⁵¹ Our exploration into this space carves a niche by zeroing in on eye-gesture control. The potential ramifications of this focus are manifold: envision surgeons wielding control over medical apparatus using mere eye movements or military strategists harnessing advanced weaponry steered by nuanced eye-gestures.⁵⁹ On a more universal scale, the prospect of redefining digital interactions for demographics like the elderly and children underscores the transformative potential of this technology. Such intuitive interfaces could make the digital realm more approachable for seniors, while simultaneously laying the foundation for a generation of children who grow up with an innate understanding of digital interactions. In summation, the dynamic synergy between AI and gesture control technology delineates a horizon teeming with opportunities.⁵⁷ From redefining accessibility to crafting specialized solutions for sectors like healthcare and defense, the canvas is vast and awaiting further nuanced strokes.⁵⁸ The coming years promise to be a crucible of innovation, with the potential to redefine the very essence of human-computer interaction. With the convergence of AI and gesture technology, we’re witnessing an evolution from simple, static gesture recognition to dynamic, context-aware systems capable of understanding intent and adapting to users’ needs. As research continues and technology matures, we can anticipate a future where gesture-based interactions become as ubiquitous and natural as using a touchscreen today.⁵³

Eye-gesture control has been a subject of increasing research interest in the field of human-computer interaction (HCI), with advancements focusing on improving system accuracy, real-time responsiveness, and practical applications. Existing gaze-based control systems have shown promising results, yet they often lack the precision needed for executing detailed commands. A real-time human-computer interaction system based on eye gazes demonstrated its potential for hands-free control applications.⁶⁰ While this system efficiently detects gaze direction, it primarily focuses on tracking movement rather than recognizing complex gestures, limiting its functionality in real-world applications requiring a broader range of commands. In contrast, our approach employs eye-gesture recognition rather than simple gaze tracking, allowing for a richer and more precise set of interactions. This enhances usability, particularly in accessibility applications where users need intuitive, fine-grained control over digital environments. Beyond gaze tracking, multi-modal interaction frameworks have been explored to enhance human-robot collaboration. A system integrating gesture and speech recognition has been developed for real-time collaboration between humans and robots.⁶¹ While multi-modal approaches offer increased interaction flexibility, they introduce higher computational complexity and require synchronized processing of multiple input streams. Our work differs by focusing solely on eye gestures, which eliminates the need for additional hardware or multi-sensor fusion while maintaining real-time responsiveness. This makes our system well-suited for environments where hands-free control is essential, such as assistive technologies and military operations. Additionally, multi-visual classification methods have been investigated for fine-grained activity recognition. A multi-visual approach integrating data from various sensor inputs has been proposed to achieve precise classification of assembly tasks.⁶² While this approach enhances classification accuracy, it often requires specialized hardware and extensive data processing, which may not be feasible for real-time applications. Our system achieves 99.63% accuracy using a lightweight software-based approach, eliminating the need for external devices while ensuring seamless performance under diverse real-world conditions. By leveraging insights from these studies, our proposed eye-gesture recognition system advances human-computer interaction by achieving higher accuracy, eliminating the reliance on specialized equipment, and offering a more efficient real-time processing pipeline.

In recent years, deep learning techniques have been widely adopted for various human-computer interaction applications, such as sentiment analysis, activity recognition, and gesture control. One notable contribution is the FSTL-SA (Few-Shot Transfer Learning for Sentiment Analysis) approach, which utilizes facial expressions to classify sentiments with high accuracy, even with limited training data.⁶³ This method demonstrates the power of transfer learning in achieving robust performance with minimal data, particularly in scenarios where labeled datasets are scarce. While FSTL-SA focuses on facial expression-based sentiment analysis, our approach applies similar principles of learning optimization to the domain of eye-gesture recognition. By using a diverse dataset and leveraging lightweight tools such as OpenCV and MediaPipe, our system achieves high accuracy (99.63%) in real-time eye gesture classification. Unlike FSTL-SA, which primarily addresses affective computing, our work focuses on enhancing human-computer interaction through gesture-based control, offering practical applications in accessibility, healthcare, and industrial automation. Furthermore, recent advancements in deep learning for visual recognition have shown the potential for multi-modal systems that integrate multiple sensory inputs to improve interaction accuracy. These approaches often require extensive computational resources and specialized hardware, limiting their practical deployment. Our system stands out by offering a software-based, hardware-independent solution, combining machine learning algorithms with efficient computational tools to achieve real-time performance without external sensors.

In comparison to existing eye-gesture control technologies, our system achieves a significantly higher accuracy of 99.63%. Prior systems, as reported in the literature, typically demonstrate accuracies ranging from 95% to 99%. However, many of these systems require specialized hardware or rely on algorithms that struggle to maintain robustness under real-world conditions, such as varying lighting or user-specific differences. Our system stands out by utilizing readily available and widely recognized tools, such as OpenCV and PyAutoGUI, which enable precise eye-movement detection and seamless command execution. Table 1 shows the comparative analysis of Eye-Gesture recognition and related systems.

Table 1. Comparative Analysis of Eye-Gesture Recognition and Related Systems.

Study	Focus	Methodology	Accuracy (%)	Hardware Requirement	Application Domain
Tanwear et al. (2020) IEEE Trans. Biomed.⁴	Wireless eye gesture control using spintronic sensors	Magnetic tunnel junction sensors and threshold-based classifier	90.8	Custom hardware (TMR sensors)	Assistive Technology
Meena et al. (2024) Multimedia Tools and Apps.⁶³	Monkeypox recognition from visuals	Deep transfer learning (InceptionV3)	98	Specialized GPU	Health Monitoring
Meena et al. (2024) Multimedia Tools and Apps.⁶⁴	Few-shot transfer learning for sentiment analysis	Few-shot learning (semi-supervised, CK+ and FER2013 datasets)	82 (60-shot)	Specialized hardware	Sentiment Analysis
Proposed System	Real-time eye-gesture recognition	AI-driven model using OpenCV, MediaPipe, PyAutoGUI	99.63	No specialized hardware	Accessibility, Assistive Tech

This approach eliminates the need for specialized hardware, making the system more accessible and cost-effective. Furthermore, the integration of advanced machine learning models enhances its adaptability to diverse demographics and scenarios, ensuring consistent performance even in challenging conditions. By addressing limitations commonly faced by existing technologies, such as slow response times and reduced accuracy in dynamic environments, our system offers a scalable, practical, and highly accurate solution for real-time eye-gesture control. This combination of simplicity, cost-effectiveness, and high performance represents a significant advancement in the field.

Methods

The prime objective of our study was to facilitate a robust methodology enabling eye gesture recognition and utilizing them to control a virtual AI eye, ultimately offering a novel approach to human-computer interaction. This methodology was delineated into a strategic, step-wise approach, ensuring a coherent progression from establishing the development environment to actual implementation and testing.

Step 1: Setting up the Development Environment: The initial step necessitated the configuration of the development environment. This comprised installing crucial Python libraries, such as OpenCV for computer vision, MediaPipe for the face mesh model, and PyAutoGUI for GUI automation, ensuring the prerequisites for video capturing, processing, and controlling mouse events through code were aptly satisfied.

Step 2: Video Capture from Webcam: Subsequent to the environment setup, the methodology focused on leveraging OpenCV to capture real-time video feeds from the user’s webcam. This enabled the system to access raw video data, which could be manipulated and analyzed to detect and interpret eye gestures.

Step 3: Frame Pre-processing: The raw video frames were subjected to pre-processing to mitigate noise and ensure the efficacy of subsequent steps. A pivotal aspect was the conversion of the frame to RGB format, which was requisite for utilizing the MediaPipe solutions.

Step 4: Eye Identification and Landmark Detection: Leveraging the MediaPipe’s face mesh solution, the system identified and mapped 468 3D facial landmarks. A particular focus was given to landmarks 474 to 478, which encompass critical points around the eye, offering pivotal data for tracking and analyzing eye movement.

Step 5: Eye Movement Tracking: Having identified the eye landmarks, the methodology pivoted towards tracking eye movement, whereby the system monitored the shift in the identified eye landmarks across consecutive frames, thereby interpreting the user’s eye gestures.

Step 6: Implementing Control through Eye Movement: Through meticulous analysis of the eye movement data, gestures were then translated into actionable commands. For instance, moving the eyes in a specific direction translated to analogous movement of a virtual AI eye, which was implemented through PyAutoGUI, offering a hands-free control mechanism.

Step 7: Additional Features and Responsiveness: Additional functionalities, such as triggering mouse clicks when certain eye gestures (like a blink) were detected, were integrated. This was achieved by meticulously analyzing specific landmarks around the eyelids and determining whether they depicted a “blink” based on positional data.

Step 8: Testing the Virtual AI Eye: Finally, the system was put through rigorous testing, ensuring the accurate interpretation of eye gestures and the responsive control of the virtual AI eye. Implementation Insight through Code: The implementation of the methodology was executed through Python code, providing a practical demonstration of how eye gestures could be captured, interpreted, and translated into control commands for a virtual AI eye. Key snippets of the code include leveraging the cv2 library for real-time video capturing and mediapipe to utilize the face mesh model which is crucial for identifying the 468 3D facial landmarks, ensuring precise detection of facial features. The identified landmarks pertinent to the eyes were then analyzed to interpret eye movement and translate it into corresponding mouse movements and clicks using the pyautogui library. In essence, the methodology employed herein offers a coherent and systematic approach towards facilitating eye-gesture-based control, ensuring not only a novel mode of human-computer interaction but also paving the way towards enhanced accessibility in digital interfaces. Figure 1 provides a description of the procedures that were followed in order to construct the AI-based eye mouse gestures. Figure 2. AI-based eye mouse gestures steps.

Figure 2. AI-based eye mouse gestures steps.

Model parameter comparison

To ensure the robustness and reliability of our proposed model, we compared its key parameters and performance metrics with several baseline models commonly used in eye-gesture recognition and related visual classification tasks. The following criteria were used for parameter comparison:

Model Architecture and Complexity: We compared the depth of the neural networks (number of layers), the number of trainable parameters, and the computational complexity (measured in FLOPs—floating point operations per second) across different models. Our proposed model strikes a balance between performance and computational efficiency, maintaining high accuracy while minimizing the number of trainable parameters, ensuring real-time responsiveness.

Learning Rate and Optimization Algorithms: Various learning rates and optimization algorithms were tested, including Adam, SGD (Stochastic Gradient Descent), and RMSprop. Adam was selected for the final model due to its superior performance in achieving faster convergence with lower validation loss.

Evaluation Metrics: The models were evaluated based on several key performance metrics, including accuracy, precision, recall, F1-score, and inference time. These metrics were calculated for each gesture type to provide a comprehensive performance assessment.

Comparison Table of Model Performance: To present the results clearly, we compiled a comparison table summarizing the performance of our proposed model and other baseline models in terms of accuracy, precision, and inference time (Table X). The results show that our model outperformed others in accuracy (99.63%) and inference speed, making it suitable for real-time applications.

Cross-Validation Results: Cross-validation was performed to compare the generalization ability of different models. The standard deviation of accuracy across folds was used as an indicator of stability and robustness. Our model demonstrated consistent performance across all validation folds, highlighting its reliability.

Dataset collection and composition

The dataset used in this study was collected from 100 volunteers, carefully selected to represent a diverse range of demographics, including variations in age, gender, and ethnicity. This diversity ensures that the model can generalize effectively across different user groups in real-world scenarios. Each participant was asked to perform 10 distinct eye gestures, such as blinking, looking left, looking right, and other commonly used gestures in human-computer interaction systems. Each gesture was repeated 20 times, resulting in a robust dataset of 20,000 gesture instances.

This comprehensive dataset was instrumental in training the AI-based eye-gesture recognition system to handle differences in eye shapes, facial structures, and dynamic lighting conditions. The participants were also asked to wear glasses, including reflective and non-reflective types, to assess the system’s adaptability to diverse visual environments.

To ensure broad generalizability, the dataset used in this study was collected from 100 volunteers, carefully selected to represent a diverse demographic composition. The participants varied across:

• Age Groups: Spanning from young adults (18–30 years) to middle-aged (31–50 years) and seniors (51+ years).
• Gender Representation: The dataset includes both male and female participants to ensure the system’s performance is not biased toward a specific gender.
• Ethnic Diversity: The participants were drawn from varied ethnic backgrounds, ensuring the model’s ability to generalize across different facial structures, eye shapes, and skin tones.

Each participant performed 10 distinct eye gestures, such as blinking, looking left, looking right, and other commonly used gestures in human-computer interaction. Each gesture was repeated 20 times, leading to a total dataset of 20,000 gesture instances. This dataset was preprocessed and split into training and testing subsets to evaluate system performance accurately. The impact of this diversity was carefully analyzed, revealing that the model achieved consistent classification accuracy across all demographic groups. No significant accuracy drop was observed among different age ranges, genders, or ethnicities, demonstrating that the system is robust and unbiased in real-world applications. Additionally, participants with glasses (both reflective and non-reflective) were included in testing, ensuring that the model remained effective under different visual conditions.

Algorithm and system development

Our algorithm distinguishes itself by implementing real-time gaze detection through advanced machine learning models specifically designed to enhance both speed and accuracy in eye-gesture recognition. Existing approaches in this field often face challenges, including slow response times, reduced accuracy in dynamic real-world environments, and limited adaptability to diverse user groups. These limitations restrict their usability in practical applications, especially in scenarios requiring real-time interaction and precision. To overcome these challenges, our system integrates OpenCV’s rapid image processing capabilities with PyAutoGUI’s intuitive interface control. OpenCV enables precise detection and tracking of facial landmarks, particularly eye movements, while PyAutoGUI translates these movements into actionable commands with minimal latency. This seamless integration ensures a fluid and responsive user experience, bridging the gap between gaze input and system execution.

Our system leverages MediaPipe’s face landmark detection for efficiency and precision. However, we have introduced custom calibration techniques that adapt the detection process to various face shapes and angles, improving the robustness and accuracy of landmark detection in real-time applications.

Model training and testing

The AI model was developed using machine learning algorithms combined with popular computer vision libraries, including OpenCV and MediaPipe. These tools enabled real-time recognition of eye gestures by capturing facial landmarks and mapping them to specific actions. The model was rigorously trained on the collected dataset to ensure robustness across various demographics and conditions. To evaluate performance, the model was tested under controlled environments with varying lighting conditions. Additionally, the participants’ use of reflective and non-reflective glasses was considered to assess the system’s adaptability to challenging visual scenarios. Performance metrics such as accuracy, precision, recall, and F1-scores were calculated to provide a comprehensive assessment of the system’s effectiveness. The model achieved an impressive accuracy rate of 99.63%, with minimal misclassification even under challenging conditions like low-light environments. A slight reduction in accuracy (to 98.9%) was observed when reflective glasses were used, highlighting an area for future refinement.

Model fine-tuning process

Fine-tuning was a critical step in optimizing our model’s performance and ensuring its adaptability to the unique characteristics of the eye-gesture dataset. After the initial training phase, we employed several strategies to refine the model and improve its accuracy and generalization capabilities:

Hyperparameter Optimization: We conducted a grid search to identify the optimal hyperparameters for the model, including the learning rate, batch size, number of epochs, and dropout rate. The final model configuration was chosen based on its performance on the validation set. For example, a learning rate of 0.001, batch size of 32, and dropout rate of 0.2 were found to provide the best balance between convergence speed and overfitting prevention.

Data Augmentation: To improve robustness and prevent overfitting, we applied data augmentation techniques such as random rotations, scaling, and flipping of the eye-gesture images. This ensured the model could handle variations in eye orientation and lighting conditions. Augmentation was particularly effective in reducing misclassification for gestures performed in less favorable conditions (e.g., participants wearing reflective glasses).

Early Stopping and Cross-Validation: We implemented early stopping to monitor validation loss and halt training when performance no longer improved. This helped prevent overfitting while maintaining high accuracy. Additionally, k-fold cross-validation (k=5) was used to evaluate the model’s stability and ensure consistent performance across different subsets of the dataset.

Performance Evaluation: The model’s fine-tuned version achieved an accuracy of 99.63% on the test set. Detailed performance metrics, including precision, recall, and F1-score, were calculated for each gesture type, confirming the model’s ability to generalize effectively across diverse user groups and environmental conditions. The confusion matrix (Figure X) highlights the classification accuracy and misclassification rates for each gesture.

Experiment Summary: Experiments were conducted using different configurations to compare the model’s performance before and after fine-tuning. The results demonstrated that fine-tuning significantly improved the system’s accuracy and reduced the error rate for complex gestures, especially under challenging conditions such as low lighting and reflective glasses.

Embedding Techniques for Eye-Gesture Recognition: To achieve accurate eye-gesture recognition, our system utilizes embedding techniques to convert eye movement data into a more structured and machine-readable format. Embeddings are essential for representing the complex spatial relationships between facial landmarks and translating these into actionable features for the learning model. Specifically, the embedding process begins by detecting key facial landmarks around the eyes using MediaPipe, which captures the x, y coordinates of these points in real time. These coordinates are then transformed into a fixed-size feature vector, representing each gesture as a numerical embedding. This vector acts as a compact representation of the gesture’s unique characteristics, preserving essential spatial relationships while reducing data dimensionality. The embedding vectors are fed into the machine learning model for classification. This approach enhances the system’s ability to differentiate between similar eye gestures, such as left glance vs. right glance, by focusing on key variations in landmark movement patterns. Additionally, the embeddings allow for efficient real-time processing, enabling the system to classify gestures accurately without significant computational overhead. Embedding techniques not only improve the robustness of our model but also ensure generalizability across diverse users by reducing noise and standardizing input features. This process plays a critical role in achieving the system’s high accuracy (99.63%) and ensuring reliable performance across different environments and user groups.

Advancements and real-world applicability

This system significantly advances the field by addressing the shortcomings of prior approaches. Traditional eye-gesture systems often report accuracies between 90% and 95%, with noticeable degradation in real-world conditions such as varied lighting or unique user-specific factors. In contrast, our model consistently demonstrates robust performance across diverse scenarios, emphasizing its reliability and adaptability. Our approach leverages cutting-edge machine learning techniques and efficient computational tools, providing a scalable and highly accurate solution for real-time eye-gesture recognition. Beyond its utility in accessibility solutions for individuals with physical impairments, the system unlocks new possibilities for intuitive control in critical applications such as assistive technologies, gaming, and gesture-controlled systems for military and rescue operations.

Results and discussion

The orchestration of our methodology propelled us into a realm of significant findings, shedding light on the functionality and efficacy of the AI-based eye mouse gesture system. Delving into the results, the findings affirm the system’s capability to competently recognize and actualize various mouse gestures with striking precision. In the realm of gesture recognition, especially clicking and scrolling, the system exhibited a pronounced accuracy of 99.6283%. The consequential evidence is demarcated by a real-world scenario, illustrated as follows: Initially, the system actively opens the camera, recognizing the user’s face to pinpoint the eyes ( Figure 3). Subsequent to that, it proficiently identifies the eyes, deciding which eye’s wink will emulate a mouse click and which eye will guide the cursor’s fixation and movement ( Figure 4). It is pivotal to note that such a high degree of accuracy not only substantiates the reliability of the system but also underscores its potential applicability in various practical scenarios. Incorporating Linear Regression, a machine learning algorithm renowned for its predictive acumen, we endeavored to enhance the system’s anticipatory capabilities concerning eye movements. Linear Regression predicates its functionality on fitting a line to eye movement and utilizing it for continuous value predictions, such as predicting the forthcoming position of the eye cursor based on previous positions.²³^,²⁴^,⁴⁶ Formally expressed as:

(1)

y = b 0 + b 1 x 1 + b 2 x2 + \dots + bnxn

Figure 3. Recognizing the user's face in order to identify the eyes.

Image taken of and by the author.

Figure 4. Identify the eyes and determine which eye will wink to squeeze the mouse and which eye the mouse will fixate.

Image taken of and by the author.

Here, “y” represents the predicted value, “x1”, “x2”,…, “xn” symbolize input features, “b0” is the intercept term, and “b1”, “b2”,…, “bn” denote coefficients that manifest the influence of each input feature on the predicted value.²⁵^,²⁶ These coefficients, extracted from training data collecting from eye movement.²⁸^,⁵²

Through 12 iterative practical testing cycles, the project substantiated its effectiveness and reliability, with outcomes depicted in equations ( 2- 20), Figures 5-8, Table 2 and Table 3. These iterative tests were indispensable for verifying the model’s robustness, ensuring its functionality, and accuracy remained steadfast across various scenarios and use-cases. The promising accuracy in recognizing and executing eye gestures poses significant implications for diverse domains, affirming the model’s potential to forge a new paradigm in hands-free control systems. The reliability ascertained from practical tests underscores its viability in real-world applications, notably in accessibility technology, gaming, and professional domains where hands-free control is pivotal. Furthermore, the practical results yield an informative base for future research, presenting avenues for enhancement and potential incorporation into varied technological ecosystems.

(2)

\sum X = 1195.54

(3)

\sum Y = 4.46

(4)

X = 99.6283

(5)

Y = 0.3717

(6)

\sum (SSX) = 0.7404

(7)

\sum (SP) = - 0.7404

(8)

Regression = ŷ = bX + a

(9)

b = SP / SSX = - 0.74 / 0.74 = - 1

(10)

a = MY - bMX = 0.37 - (- 1 * 99.63) = 100

(11)

ŷ = - 1X + 100

(12)

\hat{Y} = b 0 + b 1 X

(13)

b 1 = \frac{SPxy}{SSx} = \frac{Σ (xi - \bar{x}) (yi - \bar{y})}{Σ {(xi - \bar{x})}^{2}}

(14)

b 1 = \frac{- 0.7404}{0.7404} = - 1

(15)

b 0 = \bar{y} - b 1 \bar{x}

(16)

\bar{x} = 99.6283

(17)

\bar{y} = 0.3717

(18)

b 0 = 0.3717 + 1 * 99.6283 = 100

(19)

R 2 = \frac{SS}{SS} = \frac{Σ {(\hat{y} i - \bar{y})}^{2}}{Σ {(yi - \bar{y})}^{2}} = \frac{0.7404}{0.7404} = 1

(20)

MS = S 2 = \frac{Σ {(yi - \hat{y})}^{2}}{n - 2}

Figure 5. Plot of AI-based eye mouse gestures (accutecy).

Figure 6. Plot of AI-based eye mouse gestures (accutecy).

Figure 7. Plot of AI-based eye mouse gestures (accutecy).

Figure 8. Plot of AI-based eye mouse gestures (accutecy).

Table 2. Linear regression of AI-based eye mouse gestures.

x- $\bar{x}$	y- $\bar{y}$	(x- $\bar{x}$ )²	(x- $\bar{x}$ ) (y- $\bar{y}$ )
0.07167 -0.02833 -0.1283 -0.2283-0.1783 0.1717 -0.2483 0.3517 0.3217 -0.4283 0.3617-0.03833	-0.07167 0.02833 0.1283 0.2283 0.1783 -0.1717 0.2483 -0.3517-0.3217 0.4283-0.3617 0.03833	0.005136 0.0008028 0.01647 0.05214 0.0318 0.02947 0.06167 0.1237 0.1035 0.1835 0.1308 0.001469	-0.7404578
0	0	0.7404 (SSx)	-0.7404 (SPxy)

Table 3. Compare The AI Eye gesture control with the rest in literature.

Feature/Aspect	Our Study	Study [1]	Study [2]	Study [3]	Study [4]
Objective	Eye gesture control	Hand gesture control	Voice control	Facial recognition	Multi-modal control
Methodology	Machine learning	Deep learning	Natural language processing	Deep learning	Machine learning
Technology Used	OpenCV, PyCharm, etc.	TensorFlow, Keras	Google API, Keras	TensorFlow	OpenCV, Keras
Accuracy Level	99.63%	96%	95%	97%	99%
Key Findings	Highly accurate	Moderately accurate	Accurate with clear speech	High accuracy	High accuracy
Limitations	Limited gestures	Limited to specific gestures	Ambient noise affects accuracy	Limited expressions	Complex setup
Application Field	Healthcare, defense	Gaming, VR	Accessibility, smart home	Security, accessibility	Various fields
Future Work	Expand gesture library	Improve speed of recognition	Improve noise cancellation	Enhance recognition in varying light	Multi-modal integration

The deployment of AI-powered eye mouse gestures has unfurled a new canvas in computer accessibility, particularly for individuals experiencing motor impairments.⁶⁵ The concept revolves around the abolition of conventional input apparatus like keyboards or mice, thereby crafting a pathway through which individuals with physical disabilities can forge an effortless interaction with computer systems.²⁹ Beyond that, the implementation of eye mouse gestures augments the efficiency of computer utilization across all user spectrums, facilitating an interaction that is not only expeditious but also instinctively resonant with the user’s natural gestures.³¹^,³²^,⁶⁶ In concluding reflections, the results precipitated from our nuanced methodology and exhaustive practical evaluations unveil a system punctuated by adept proficiency in recognizing and meticulously interpreting eye gestures. This not merely propels us along a trajectory towards crafting more perceptive, inclusive, and adaptive mechanisms of human-computer interaction but also magnifies the richness enveloping user experiences. Furthermore, it unfolds an expansive horizon wherein technological accessibility and interactivity are not just theoretical constructs but tangible realities, perceptible in everyday interactions. The implications of these findings reverberate across multiple spectrums. Within the specialized field of accessibility technology, the innovation opens a new chapter where constraints are minimized and potentialities maximized. In wider contexts, the applicability spans from enhancing gaming experiences to refining professional interfaces, where rapid, intuitive control is paramount. Engaging with technology is poised to transcend conventional boundaries, where the symbiosis between user intention and technological response is seamlessly interwoven through the fabric of intuitive design and intelligent response. Therefore, the avenues unfurling ahead are not merely extensions of the present capabilities but rather, the precursors to a new era wherein technological interaction is a harmonious blend of intuition, inclusivity, and immersive experience. As we navigate through these exciting trajectories, our findings lay down a foundational stone upon which future research can build, innovate, and continue to redefine the limits of what is possible within the realm of AI-enhanced gesture control technology, propelling us toward a future where technology is not just interacted with but is intuitively entwined with user intention and accessibility.

The dataset used in this study was collected from 100 volunteers, each representing a diverse range of demographics, including variations in age, gender, and ethnicity, to ensure broad generalizability. Each participant performed 10 distinct eye gestures, with each gesture being repeated 20 times, resulting in a total dataset of 20,000 gesture instances. This diversity was crucial in training the AI model to accurately capture and handle differences in eye shapes, facial structures, and movement dynamics. The system achieved an accuracy rate of 99.63%, with precision and recall rates of 99.5% and 99.7%, respectively. The robustness of the system was further demonstrated through its consistent performance under varying lighting conditions and with participants wearing glasses. There was a slight reduction in accuracy (to 98.9%) when reflective glasses were worn, indicating that minor refinements could improve performance in such scenarios. However, we acknowledge the importance of further validating the system using publicly available datasets for broader generalizability. We are currently exploring the integration of external datasets, such as those from Dryad, to enhance the comparative analysis and robustness of our model. These results confirm the system’s ability to generalize effectively across different user groups and conditions, making it highly applicable for real-world applications, particularly in accessibility solutions and hands-free control systems.

To further illustrate the system’s performance in recognizing and correctly classifying eye gestures, a confusion matrix was generated, as shown in Figure X. The matrix highlights the classification accuracy for each of the 10 distinct eye gestures and indicates where misclassifications occurred. Table 4 shows the Confusion matrix for eye gesture recognition.

Table 4. Confusion matrix for eye gesture recognition.

	Blink	Left glance	Right glance	Upward glance	Downward glance
True blink	99.80%	0.10%	0.10%	0.00%	0.00%
True left glance	0.00%	99.70%	0.20%	0.00%	0.10%
True right glance	0.10%	0.10%	99.70%	0.00%	0.10%
True upward glance	0.00%	0.00%	0.00%	99.80%	0.10%
True downward glance	0.00%	0.10%	0.10%	0.10%	99.70%

The confusion matrix reveals that the system performed exceptionally well in distinguishing between different gestures, with minimal misclassification errors. For instance, the system had a classification accuracy of 99.8% for blink gestures, and minor misclassification errors were observed between gestures like left and right glances. These small errors were likely due to the similarity in gesture direction, but the overall classification performance remained robust, with an average accuracy rate of 99.63% across all gestures. While the system’s accuracy is impressive, long-term usability raises potential concerns about eye strain during extended sessions. To mitigate this, we recommend incorporating periodic calibration breaks and exploring adaptive interfaces that adjust based on user fatigue, ensuring comfort over longer periods.

User comfort and long-term usability

Prolonged use of eye-gesture recognition systems can introduce concerns regarding eye strain and fatigue, particularly in continuous operation scenarios. Given that eye tracking relies on sustained gaze movement and fixation, users may experience discomfort over extended periods, affecting usability and engagement.

To mitigate these challenges, we propose several strategies:

1. Adaptive Sensitivity Adjustments: The system can dynamically adjust its responsiveness based on detected user fatigue levels. By integrating machine learning models that monitor blink rates and gaze stability, the interface can adapt by reducing required gesture intensity, thereby minimizing strain.
2. Periodic Calibration Breaks: Implementing automatic rest reminders at optimal intervals will encourage users to take short breaks, preventing prolonged strain. These breaks can be based on user activity patterns, ensuring that engagement remains efficient without causing discomfort.
3. Customizable Interaction Modes: Offering users the ability to modify gesture sensitivity and response time allows for personalized interaction, catering to individual comfort levels and preferences. This ensures the system remains accessible and adaptable across different user needs.

Challenges and limitations

Challenges in the work

In the development of our AI-driven eye-gesture recognition system, we encountered several challenges:

1. Variability in Eye Gestures: Participants exhibited differences in performing eye gestures due to factors such as individual physiology, cultural differences, and varying levels of familiarity with the gestures. This variability posed challenges in achieving consistent recognition across all users.
2. Environmental Influences: External factors, including lighting conditions and background environments, affected the accuracy of eye gesture detection. For instance, changes in ambient light could alter the appearance of eye features, leading to potential misclassification.
3. Real-Time Processing Constraints: Implementing the system to operate in real-time required optimizing algorithms to minimize latency. High computational demands could lead to delays, affecting the user experience.

Challenges in the Dataset

The dataset used in this study also presented specific challenges:

1. Data Imbalance: Certain eye gestures were underrepresented in the dataset, leading to a class imbalance. This imbalance could bias the model towards more frequent gestures, reducing recognition accuracy for less common ones.
2. Blink-Related Data Gaps: Natural eye blinks introduced missing data points, which could disrupt the continuity of gesture sequences and affect the system’s performance.
3. Calibration Drift: Over time, calibration of eye-tracking equipment can degrade due to factors like participant movement or device slippage, leading to inaccuracies in data collection.

Privacy and data protection

Given the sensitive nature of eye-tracking data, we have prioritized robust privacy and data protection measures to ensure user trust and compliance with international standards. All collected eye-movement data is anonymized through strict protocols, ensuring that no personally identifiable information (PII) is associated with the stored data. Additionally, data is encrypted both during transmission and at rest, safeguarding it from unauthorized access or breaches. Our system is designed to adhere to globally recognized privacy regulations, including the General Data Protection Regulation (GDPR). By implementing these frameworks, we ensure that data collection, storage, and processing meet the highest standards of privacy and security. These measures not only protect users but also enable the safe and ethical deployment of the system in sensitive environments, such as healthcare and assistive technologies. Future updates will continue to prioritize privacy innovations, further enhancing user confidence and compliance across broader contexts.

Conclusion

In this research, we have not only demonstrated but also underscored the compelling efficacy of AI-powered eye-gesture recognition for computer system control, achieving a noteworthy accuracy pinnacle of 99.6283%. Through an intricate synergy of eye-tracking technology and machine learning algorithms, a system has been sculpted, proficient at decoding the nuanced ballet of eye movements and flawlessly translating them into user-intended computational actions. The repercussions of this technological advance cascade beyond merely enhancing computer accessibility — it stands on the brink of universally redefining user efficiency and interactive experiences. Employing a suite of tools, including PyCharm, OpenCV, mediapipe, and pyautogui, we have sculpted a foundational framework that invites the seamless integration of such technologies into a plethora of advanced applications. This expands from inclusive computing interfaces to intricate applications such as nuanced weapon control through body gestures. The vista ahead is rife with possibilities and we, therefore, beacon the research community to plummet deeper into the expansive oceans of artificial intelligence and machine learning. By strategically integrating and adventuring through additional Python libraries and exploring diverse applications, we envision a future where transformative advancements permeate myriad sectors, notably healthcare and defense. While the proposed system achieves a high accuracy rate of 99.63% and demonstrates robustness across diverse scenarios, certain challenges remain. In scenarios involving reflective glasses, a minor accuracy reduction to 98.9% was observed, suggesting areas for further optimization. Additionally, while the system was evaluated using a robust dataset collected from 100 volunteers, validation using publicly available datasets would further enhance its generalizability. Addressing these aspects in future work will strengthen the system’s applicability and reliability in broader contexts. As we conclude, it’s imperative to reflect upon the universal axiom that technological progression is an ever-evolving journey. While we celebrate the milestones achieved through this research, it is pivotal to perceive them not as a terminus, but as a launchpad from which further explorations, innovations, and refinements can take flight. Thus, the canvases of healthcare, defense, and beyond await the strokes of further innovations, promising a future where technology and human intent meld into a seamlessly interactive and intuitive tapestry, crafting experiences that are not merely used but lived. Consequently, our journey propels forward, with an ever-vigilant eye towards a horizon where technology becomes an unspoken extension of our intentions, enabling a world wherein interaction is as effortless as a mere blink of an eye.

Future work

While our proposed eye-gesture recognition system demonstrates high accuracy and usability, there are several avenues for future improvement:

1. Expansion of Gesture Vocabulary: Currently, the system recognizes a limited set of eye gestures. Future work could focus on expanding the gesture vocabulary to include more complex and subtle eye movements, enhancing the system’s functionality.
2. User Adaptation and Personalization: Implementing adaptive algorithms that tailor the system to individual user behaviors and preferences could improve accuracy and user satisfaction.
3. Integration with Other Modalities: Combining eye-gesture recognition with other input modalities, such as voice commands or hand gestures, could create a more robust and versatile human-computer interaction system.
4. Real-World Testing and Validation: Conducting extensive real-world testing across diverse environments and user groups would help validate the system’s performance and identify areas for refinement.
5. Hardware Optimization: Exploring the use of specialized hardware, such as dedicated eye-tracking devices, could enhance system responsiveness and reduce latency.

Ethical and informed consent for data usage

The research has been autonomously conducted by the author in a controlled environment, utilizing his technical proficiency to design and implement the proposed methodology. Therefore, it is crucial to emphasize that no external permissions or collaborations were required or solicited throughout the research journey. The author has consistently adhered to ethical guidelines and data protection norms, ensuring the maintenance of the pinnacle of ethical research practices throughout the investigation.

Data availability

Zenodo: Nachaat3040/Eye-Gesture-: Eye-Gesture- 1.0, https://doi.org/10.5281/zenodo.10185053.⁶⁷

This project contains the following underlying data:

- Code of Eye-Gesture Control of Computer Systems via Artificial Intelligence.txt
- Data generated.txt

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgment

Dr. Nachaat Mohamed, the author, extends his profound thanks to Rabdan Academy in the United Arab Emirates for their generous financial support post-acceptance of this research project. Additionally, Dr. Mohamed expresses his earnest gratitude towards the editors and reviewers who invested their time and expertise to diligently review and fortify this research. Their insightful critiques and invaluable suggestions have substantially augmented the overall scientific rigor and quality of this investigation.

References

1. Kirbis M, Kramberger I: Mobile device for electronic eye gesture recognition. IEEE Trans. Consum. Electron. November 2009; 55(4): 2127–2133. Publisher Full Text
2. Pingali TR, et al.: Eye-gesture controlled intelligent wheelchair using Electro-Oculography. 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, VIC, Australia. 2014; pp. 2065–2068. Publisher Full Text
3. Tanwear A, et al.: Spintronic Sensors Based on Magnetic Tunnel Junctions for Wireless Eye Movement Gesture Control. IEEE Trans. Biomed. Circuits Syst. Dec. 2020; 14(6): 1299–1310. PubMed Abstract | Publisher Full Text
4. Marina-Miranda J, Traver VJ: Head and Eye Egocentric Gesture Recognition for Human-Robot Interaction Using Eyewear Cameras. IEEE Robot. Autom. Lett. July 2022; 7(3): 7067–7074. Publisher Full Text
5. Lin M, Li B: A wireless EOG-based Human Computer Interface. 2010 3rd International Conference on Biomedical Engineering and Informatics, Yantai, China. 2010; pp. 1794–1796. Publisher Full Text
6. Morency L-P, Quattoni A, Darrell T: Latent-Dynamic Discriminative Models for Continuous Gesture Recognition. 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA. 2007; pp. 1–8. Publisher Full Text
7. Kulkarni G, Kulkarni R, Deshmukh V: Eye Gesture Interface and Emotion Detection Tool. 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India. 2023; pp. 1–6. Publisher Full Text
8. Liang Y, Samtani S, Guo B, et al.: Behavioral biometrics for continuous authentication in the internet-of-things era: An artificial intelligence perspective. IEEE Internet Things J. 2020; 7(9): 9128–9143. Publisher Full Text
9. Wei L, Lin Y, Wang J, et al.: Time-frequency convolutional neural network for automatic sleep stage classification based on single-channel EEG. 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), IEEE. 2017, November; pp. 88–95.
10. Zheng WL, Liu W, Lu Y, et al.: Emotionmeter: A multimodal framework for recognizing human emotions. IEEE Trans. Cybern. 2018; 49(3): 1110–1122. PubMed Abstract | Publisher Full Text
11. Hossain Z, Shuvo MMH, Sarker P: Hardware and software implementation of real time electrooculogram (EOG) acquisition system to control computer cursor with eyeball movement. 2017 4th international conference on advances in electrical engineering (ICAEE), IEEE. 2017, September; pp. 132–137.
12. Singh J, Aggarwal R, Tiwari S, et al.: Exam Proctoring Classification Using Eye Gaze Detection. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), IEEE. 2022, October; pp. 371–376.
13. Venugopal D, Amudha J, Jyotsna C: Developing an application using eye tracker. 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE. 2016, May; pp. 1518–1522.
14. Mohamed N, Awasthi A, Kulkarni N, et al.: Decision Tree Based Data Pruning with the Estimation of Oversampling Attributes for the Secure Communication in IOT. Int. J. Intell. Syst. Appl. Eng. 2022; 10(2s): 212–216.
15. Mohammadpour M, Hashemi SMR, Houshmand N: Classification of EEG-based emotion for BCI applications. 2017 Artificial Intelligence and Robotics (IRANOPEN), IEEE. 2017, April; pp. 127–131.
16. Lawson AP, Mayer RE: The power of voice to convey emotion in multimedia instructional messages. Int. J. Artif. Intell. Educ. 2022; 32(4): 971–990. Publisher Full Text
17. Thapaliya S, Jayarathna S, Jaime M: Evaluating the EEG and eye movements for autism spectrum disorder. 2018 IEEE International Conference on Big Data (Big Data), IEEE. 2018, December; pp. 2328–2336.
18. Mohamed N: Study of bypassing Microsoft Windows Security using the MITRE CALDERA Framework. F1000Res. 2022; 11: 422. PubMed Abstract | Publisher Full Text | Free Full Text
19. Wang KJ, Zheng CY, Mao ZH: Human-centered, ergonomic wearable device with computer vision augmented intelligence for VR multimodal human-smart home object interaction. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), IEEE. 2019, March; pp. 767–768.
20. Liang X, Ghannam R, Heidari H: Wrist-worn gesture sensing with wearable intelligence. IEEE Sensors J. 2018; 19(3).
21. Holmes M, Latham A, Crockett K, et al.: Near real-time comprehension classification with artificial neural networks: Decoding e-learner non-verbal behavior. IEEE Trans. Learn. Technol. 2017; 11(1): 5–12. Publisher Full Text
22. Fridman L, Langhans P, Lee J, et al.: Driver gaze region estimation without use of eye movement. IEEE Intell. Syst. 2016; 31(3): 49–56. Publisher Full Text
23. Alam MM, Raihan MMS, Chowdhury MR, et al.: High Precision Eye Tracking Based on Electrooculography (EOG) Signal Using Artificial Neural Network (ANN) for Smart Technology Application. 2021 24th International Conference on Computer and Information Technology (ICCIT), IEEE. 2021, December; pp. 1–6.
24. Lee TM, Yoon JC, Lee IK: Motion sickness prediction in stereoscopic videos using 3d convolutional neural networks. IEEE Trans. Vis. Comput. Graph. 2019; 25(5): 1919–1927. PubMed Abstract | Publisher Full Text
25. Zhang J, Tao D: Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet Things J. 2020; 8(10): 7789–7817. Publisher Full Text
26. Abiodun OI, Jantan A, Omolara AE, et al.: State-of-the-art in artificial neural network applications: A survey. Heliyon. 2018; 4(11): e00938. PubMed Abstract | Publisher Full Text | Free Full Text
27. Khosravi N, Abdolvand A, Oubelaid A, et al.: Improvement of power quality parameters using modulated-unified power quality conditioner and switched-inductor boost converter by the optimization techniques for a hybrid AC/DC microgrid. Sci. Rep. 2022; 12(1): 1–20. Publisher Full Text
28. Mohamed NA, Jantan A, Abiodun OI: An improved behaviour specification to stop advanced persistent threat on governments and organizations network. Proceedings of the International MultiConference of Engineers and Computer Scientists. 2018; Vol. 1: pp. 14–16.
29. Mohamed N, Alam E, Stubbs GL: Multi-layer protection approach MLPA for the detection of advanced persistent threat. J. Posit. School Psychol. 2022; 4496–4518.
30. Omolara AE, Jantan A, Abiodun OI, et al.: Fingereye: improvising security and optimizing ATM transaction time based on iris-scan authentication. Int. J. Electr. Comput. Eng. 2019; 9(3): 1879. Publisher Full Text
31. Mohamed N: State-of-the-Art in Chinese APT Attack and Using Threat Intelligence for Detection. A Survey. J. Posit. School Psychol. 2022; 4419–4443.
32. Mohamed N, Almazrouei SK, Oubelaid A, et al.: Air-Gapped Networks: Exfiltration without Privilege Escalation for Military and Police Units. Wirel. Commun. Mob. Comput. 2022; 2022: 1–11. Publisher Full Text
33. Oubelaid A, Taib N, Nikolovski S, et al.: Intelligent speed control and performance investigation of a vector controlled electric vehicle considering driving cycles. Electronics. 2022; 11(13): 1925. Publisher Full Text
34. Mohamed N, Kumar KS, Sharma S, et al.: Wireless Sensor Network Security with the Probability Based Neighbourhood Estimation. Int. J. Intell. Syst. Appl. Eng. 2022; 10(2s): 231–235.
35. Mohamed NA, Jantan A, Abiodun OI: Protect Governments, and organizations Infrastructure against Cyber Terrorism (Mitigation and Stop of Server Message Block (SMB) Remote Code Execution Attack). Int. J. Eng. 2018; 11(2): 261–272.
36. Yao L, Park M, Grag S, et al.: Eye Movement and Visual Target Synchronization Level Detection Using Deep Learning. Australasian Joint Conference on Artificial Intelligence. Cham: Springer; 2022, February; pp. 668–678.
37. Yaneva V, Eraslan S, Yesilada Y, et al.: Detecting high-functioning autism in adults using eye tracking and machine learning. IEEE Trans. Neural Syst. Rehabil. Eng. 2020; 28(6): 1254–1261. PubMed Abstract | Publisher Full Text
38. Kumar BV, Srinivas KK, Anudeep P, et al.: Artificial Intelligence Based Algorithms for Driver Distraction Detection: A Review. 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), IEEE. 2021, October; pp. 383–386.
39. Lin W, Kotakehara Y, Hirota Y, et al.: Modeling reading behaviors: An automatic approach to eye movement analytics. IEEE Access. 2021; 9: 63580–63590. Publisher Full Text
40. Jiang M, Francis SM, Srishyla D, et al.: Classifying individuals with ASD through facial emotion recognition and eye-tracking. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE. 2019, July; pp. 6063–6068.
41. Putra RY, Kautsar S, Adhitya RY, et al.: Neural network implementation for invers kinematic model of arm drawing robot. 2016 International Symposium on Electronics and Smart Devices (ISESD), IEEE. 2016, November; pp. 153–157.
42. Ramkumar S, Kumar KS, Emayavaramban G: A feasibility study on eye movements using electrooculogram based HCI. 2017 International Conference on Intelligent Sustainable Systems (ICISS), IEEE. 2017, December; pp. 380–383.
43. Li L, Godaba H, Ren H, et al.: Bioinspired soft actuators for eyeball motions in humanoid robots. IEEE/ASME Transactions on Mechatronics. 2018; 24(1): 100–108. Publisher Full Text
44. Castellanos JL, Gomez MF, Adams KD: Using machine learning based on eye gaze to predict targets: An exploratory study. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE. 2017, November; pp. 1–7.
45. Wang W, Lee J, Harrou F, et al.: Early detection of Parkinson’s disease using deep learning and machine lea.2020.
46. Akshay S, Megha YJ, Shetty CB: Machine learning algorithm to identify eye movement metrics using raw eye tracking data. 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE. 2020, August; pp. 949–955.
47. Ramzan M, Khan HU, Awan SM, et al.: A survey on state-of-the-art drowsiness detection techniques. IEEE Access. 2019; 7: 61904–61919. Publisher Full Text
48. Kruthiventi SS, Ayush K, Babu RV: Deepfix: A fully convolutional neural network for predicting human eye fixations. IEEE Trans. Image Process. 2017; 26(9): 4446–4456. PubMed Abstract | Publisher Full Text
49. Challa KNR, Pagolu VS, Panda G, et al.: An improved approach for prediction of Parkinson’s disease using machine learning techniques. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), IEEE. 2016, October; pp. 1446–1451.
50. Mohammadpour M, Khaliliardali H, Hashemi SMR, et al.: Facial emotion recognition using deep convolutional networks. 2017 IEEE 4th international conference on knowledge-based engineering and innovation (KBEI), IEEE. 2017, December; pp. 0017–0021.
51. Lemley J, Kar A, Drimbarean A, et al.: Convolutional neural network implementation for eye-gaze estimation on low-quality consumer imaging systems. IEEE Trans. Consum. Electron. 2019; 65(2): 179–187. Publisher Full Text
52. Mohamed N, Belaton B: SBI model for the detection of advanced persistent threat based on strange behavior of using credential dumping technique. IEEE Access. 2021; 9: 42919–42932. Publisher Full Text
53. Mohamed NA, Jantan A, Omolara AE: Mitigation of Cyber Terrorism at ATMs, and Using DNA, Fingerprint, Mobile Banking App to withdraw cash (Connected with IoT).
54. Mohamed NAE, Jantan A, Omolara AE: Mitigation of Cyber Terrorism at ATMs, and Using DNA, Fingerprint, Mobile Banking App to withdraw cash (Connected with IoT).
55. Jie L, Jian C, Lei W: Design of multi-mode UAV human-computer interaction system. 2017 IEEE international conference on unmanned systems (ICUS), IEEE. 2017, October; pp. 353–357.
56. Oravec JA: The emergence of “truth machines”?: Artificial intelligence approaches to lie detection. Ethics Inf. Technol. 2022; 24(1): 1–10. Publisher Full Text
57. Mengi M, Malhotra D: Artificial intelligence based techniques for the detection of socio-behavioral disorders: a systematic review. Arch. Comput. Methods Eng. 2022; 29(5): 2811–2855. Publisher Full Text
58. Yao L, Park M, Grag S, et al.: Eye Movement and Visual Target Synchronization Level Detection Using Deep Learning. Australasian Joint Conference on Artificial Intelligence. Cham: Springer; 2022, February; pp. 668–678.
59. Wang KJ, Liu Q, Zhao Y, et al.: Intelligent wearable virtual reality (VR) gaming controller for people with motor disabilities. 2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), IEEE. 2018, December; pp. 161–164.
60. Chen H, Zendehdel N, Leu MC, et al.: Real-time human-computer interaction using eye gazes. Manuf. Lett. 2023; 35: 883–894. Publisher Full Text
61. Chen H, Leu MC, Yin Z: Real-time multi-modal human–robot collaboration using gestures and speech. J. Manuf. Sci. Eng. 2022; 144(10):101007. Publisher Full Text
62. Chen H, Zendehdel N, Leu MC, et al.: Fine-grained activity classification in assembly based on multi-visual modalities. J. Intell. Manuf. 2024; 35(5): 2215–2233. Publisher Full Text
63. Meena G, Mohbey KK, Lokesh K: FSTL-SA: few-shot transfer learning for sentiment analysis from facial expressions. Multimed. Tools Appl. 2024: 1–29. Publisher Full Text
64. Meena G, Mohbey KK, Kumar S: Monkeypox recognition and prediction from visuals using deep transfer learning-based neural networks. Multimed. Tools Appl. 2024; 83: 71695–71719. Publisher Full Text
65. Belaid S, Rekioua D, Oubelaid A, et al.: A power management control and optimization of a wind turbine with battery storage system. J. Energy Storage. 2022; 45: 103613. Publisher Full Text
66. Rong Y, Han C, Hellert C, et al.: Artificial intelligence methods in in-cabin use cases: a survey. IEEE Intell. Transp. Syst. Mag. 2021; 14(3): 132–145. Publisher Full Text
67. Dr. Mohamed N : Nachaat3040/Eye-Gesture-: Eye-Gesture- 1.0 (AI). [Dataset]. Zenodo. 2023. Publisher Full Text

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Version 3

VERSION 3 PUBLISHED 19 Feb 2024

Author details Author details

HLS, Rabdan Academy, Abu Dhabi, Abu Dhabi, United Arab Emirates

Competing interests

No competing interests were disclosed.

Grant information

This work was supported by Rabdan Academy.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (3)

version 3

Revised

Published: 25 Feb 2025, 13:109

https://doi.org/10.12688/f1000research.144962.3

version 2

Revised

Published: 18 Dec 2024, 13:109

https://doi.org/10.12688/f1000research.144962.2

version 1

Published: 19 Feb 2024, 13:109

https://doi.org/10.12688/f1000research.144962.1

© 2025 Mohamed N. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mohamed N. Eye-gesture control of computer systems via artificial intelligence [version 3; peer review: 1 approved, 2 approved with reservations]. F1000Research 2025, 13:109 (https://doi.org/10.12688/f1000research.144962.3)

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ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions

Version 3

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PUBLISHED 25 Feb 2025

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Reviewer Report 03 Mar 2025

Gaurav Meena, Central University of Rajasthan, Ajmer, India

Approved

https://doi.org/10.5256/f1000research.178296.r368421

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Version 2

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PUBLISHED 18 Dec 2024

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Reviewer Report 24 Jan 2025

Gaurav Meena, Central University of Rajasthan, Ajmer, India

Approved with Reservations

https://doi.org/10.5256/f1000research.175922.r360627

The manuscript presents several significant issues that need to be addressed before it can be considered for indexing. Please find my detailed comments and concerns below:
1. Please add two paragraphs in the introduction: a) objectives and motivations tied to gaps in the literature; b) research questions.
2. The literature review of the article doesn't provide comprehensive coverage of related work. Most troublesome is the significant gap in coverage of the state-of-the-art. A few important pieces of literature about deep learning are not cited, for example.
(a). FSTL-SA: few-shot transfer learning for sentiment analysis from facial expressions, Meena G .et.al., 2024 (Ref 1)
3. It is not clear how the authors used the embedding techniques. Please provide more explanations.
4. It is not clear how the authors fine-tuned the model. Please add experiments and details.
5. It is not clear how the parameters of the models are compared.
6. I would strongly advise including all major works in 2023 and 2024 and drawing a tabular comparison among your work and other works. A few works worth comparing, referring to, or citing are below:
(b). Monkeypox recognition and prediction from visuals using deep transfer learning-based neural networks, Meena G .et.al., 2024 (Ref 2)
7. There is no such sub-section mentioning the details about the dataset size. I couldn’t find it anywhere regarding the size or quantity of the dataset.
8. I'd recommend adding some possible improvements to the proposed approach.
9. What are the avenues for future research or improvements identified based on this study?
10. The challenges in the work need to be stated (As mentioned in the Title)
11. What are the challenges in the dataset?

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Meena G, Mohbey K, Lokesh K: FSTL-SA: few-shot transfer learning for sentiment analysis from facial expressions. Multimedia Tools and Applications. 2024. Publisher Full Text
2. Meena G, Mohbey K, Kumar S: Monkeypox recognition and prediction from visuals using deep transfer learning-based neural networks. Multimedia Tools and Applications. 2024; 83 (28): 71695-71719 Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Deep Learning, Artificial Intelligence

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Author Response 25 Feb 2025

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

25 Feb 2025

Author Response
Dear Dr. Meena,
We sincerely appreciate your thorough review and valuable feedback on our manuscript. Your insights have been instrumental in enhancing the quality and clarity of our work. Below, ... Continue reading
Dear Dr. Meena,
We sincerely appreciate your thorough review and valuable feedback on our manuscript. Your insights have been instrumental in enhancing the quality and clarity of our work. Below, we address each of your comments in detail:
1. Introduction Enhancements:

Objectives and Motivations: We have expanded the introduction to include a detailed discussion of the objectives and motivations of our study, explicitly linking them to identified gaps in the existing literature.

Research Questions: A new subsection outlining the specific research questions guiding our investigation has been added to provide clarity on the study's focus.

2. Comprehensive Literature Review:

We have conducted an extensive review of recent literature, particularly focusing on state-of-the-art developments in deep learning. Notably, we have included the following reference to enrich our discussion:

Meena, G., et al. (2024). "FSTL-SA: Few-Shot Transfer Learning for Sentiment Analysis from Facial Expressions."

3. Embedding Techniques Explanation:

We have elaborated on the embedding techniques utilized in our study, providing a comprehensive explanation of their implementation and role within our methodology.

4. Model Fine-Tuning Details:

Detailed descriptions of the model fine-tuning process have been incorporated, including the specific experiments conducted and the parameters adjusted to optimize performance.

5. Model Parameter Comparison:

We have clarified the comparative analysis of model parameters, outlining the criteria and metrics used to evaluate and contrast the performance of different models.

6. Inclusion of Recent Works and Comparative Analysis:

Our literature review now encompasses major works from 2023 and 2024. We have also included a comparative table that juxtaposes our findings with those of recent studies, including:

Meena, G., et al. (2024). "Monkeypox Recognition and Prediction from Visuals Using Deep Transfer Learning-Based Neural Networks."

7. Dataset Size Specification:

A subsection titled "Dataset Description" has been added to the Methodology section, detailing the size and composition of the dataset used in our study.

8. Proposed Approach Improvements:

We have included a discussion on potential improvements to our proposed approach, highlighting areas for enhancement and refinement.

9. Future Research Directions:

A "Future Work" subsection has been added to outline avenues for future research, building upon the findings of our current study.

10. Challenges in the Work:

We have articulated the challenges encountered during our research, providing a candid discussion of the obstacles faced and how they were addressed.

11. Dataset Challenges:

The "Dataset Description" subsection also includes an analysis of the challenges inherent in our dataset, discussing limitations and potential impacts on our findings.

We trust that these revisions comprehensively address your concerns. Your constructive feedback has been invaluable in strengthening our manuscript, and we are grateful for your thoughtful suggestions.
Warm regards,
Dr. Nachaat Mohamed
Dear Dr. Meena,
We sincerely appreciate your thorough review and valuable feedback on our manuscript. Your insights have been instrumental in enhancing the quality and clarity of our work. Below, we address each of your comments in detail:
1. Introduction Enhancements:

Objectives and Motivations: We have expanded the introduction to include a detailed discussion of the objectives and motivations of our study, explicitly linking them to identified gaps in the existing literature.

Research Questions: A new subsection outlining the specific research questions guiding our investigation has been added to provide clarity on the study's focus.

2. Comprehensive Literature Review:

We have conducted an extensive review of recent literature, particularly focusing on state-of-the-art developments in deep learning. Notably, we have included the following reference to enrich our discussion:

Meena, G., et al. (2024). "FSTL-SA: Few-Shot Transfer Learning for Sentiment Analysis from Facial Expressions."

3. Embedding Techniques Explanation:

We have elaborated on the embedding techniques utilized in our study, providing a comprehensive explanation of their implementation and role within our methodology.

4. Model Fine-Tuning Details:

Detailed descriptions of the model fine-tuning process have been incorporated, including the specific experiments conducted and the parameters adjusted to optimize performance.

5. Model Parameter Comparison:

We have clarified the comparative analysis of model parameters, outlining the criteria and metrics used to evaluate and contrast the performance of different models.

6. Inclusion of Recent Works and Comparative Analysis:

Our literature review now encompasses major works from 2023 and 2024. We have also included a comparative table that juxtaposes our findings with those of recent studies, including:

Meena, G., et al. (2024). "Monkeypox Recognition and Prediction from Visuals Using Deep Transfer Learning-Based Neural Networks."

7. Dataset Size Specification:

A subsection titled "Dataset Description" has been added to the Methodology section, detailing the size and composition of the dataset used in our study.

8. Proposed Approach Improvements:

We have included a discussion on potential improvements to our proposed approach, highlighting areas for enhancement and refinement.

9. Future Research Directions:

A "Future Work" subsection has been added to outline avenues for future research, building upon the findings of our current study.

10. Challenges in the Work:

We have articulated the challenges encountered during our research, providing a candid discussion of the obstacles faced and how they were addressed.

11. Dataset Challenges:

The "Dataset Description" subsection also includes an analysis of the challenges inherent in our dataset, discussing limitations and potential impacts on our findings.

We trust that these revisions comprehensively address your concerns. Your constructive feedback has been invaluable in strengthening our manuscript, and we are grateful for your thoughtful suggestions.
Warm regards,
Dr. Nachaat Mohamed
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 25 Feb 2025

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

25 Feb 2025

Author Response
Dear Dr. Meena,
We sincerely appreciate your thorough review and valuable feedback on our manuscript. Your insights have been instrumental in enhancing the quality and clarity of our work. Below, ... Continue reading
Dear Dr. Meena,
We sincerely appreciate your thorough review and valuable feedback on our manuscript. Your insights have been instrumental in enhancing the quality and clarity of our work. Below, we address each of your comments in detail:
1. Introduction Enhancements:

Objectives and Motivations: We have expanded the introduction to include a detailed discussion of the objectives and motivations of our study, explicitly linking them to identified gaps in the existing literature.

Research Questions: A new subsection outlining the specific research questions guiding our investigation has been added to provide clarity on the study's focus.

2. Comprehensive Literature Review:

We have conducted an extensive review of recent literature, particularly focusing on state-of-the-art developments in deep learning. Notably, we have included the following reference to enrich our discussion:

Meena, G., et al. (2024). "FSTL-SA: Few-Shot Transfer Learning for Sentiment Analysis from Facial Expressions."

3. Embedding Techniques Explanation:

We have elaborated on the embedding techniques utilized in our study, providing a comprehensive explanation of their implementation and role within our methodology.

4. Model Fine-Tuning Details:

Detailed descriptions of the model fine-tuning process have been incorporated, including the specific experiments conducted and the parameters adjusted to optimize performance.

5. Model Parameter Comparison:

We have clarified the comparative analysis of model parameters, outlining the criteria and metrics used to evaluate and contrast the performance of different models.

6. Inclusion of Recent Works and Comparative Analysis:

Our literature review now encompasses major works from 2023 and 2024. We have also included a comparative table that juxtaposes our findings with those of recent studies, including:

Meena, G., et al. (2024). "Monkeypox Recognition and Prediction from Visuals Using Deep Transfer Learning-Based Neural Networks."

7. Dataset Size Specification:

A subsection titled "Dataset Description" has been added to the Methodology section, detailing the size and composition of the dataset used in our study.

8. Proposed Approach Improvements:

We have included a discussion on potential improvements to our proposed approach, highlighting areas for enhancement and refinement.

9. Future Research Directions:

A "Future Work" subsection has been added to outline avenues for future research, building upon the findings of our current study.

10. Challenges in the Work:

We have articulated the challenges encountered during our research, providing a candid discussion of the obstacles faced and how they were addressed.

11. Dataset Challenges:

The "Dataset Description" subsection also includes an analysis of the challenges inherent in our dataset, discussing limitations and potential impacts on our findings.

We trust that these revisions comprehensively address your concerns. Your constructive feedback has been invaluable in strengthening our manuscript, and we are grateful for your thoughtful suggestions.
Warm regards,
Dr. Nachaat Mohamed
Dear Dr. Meena,
We sincerely appreciate your thorough review and valuable feedback on our manuscript. Your insights have been instrumental in enhancing the quality and clarity of our work. Below, we address each of your comments in detail:
1. Introduction Enhancements:

Objectives and Motivations: We have expanded the introduction to include a detailed discussion of the objectives and motivations of our study, explicitly linking them to identified gaps in the existing literature.

Research Questions: A new subsection outlining the specific research questions guiding our investigation has been added to provide clarity on the study's focus.

2. Comprehensive Literature Review:

We have conducted an extensive review of recent literature, particularly focusing on state-of-the-art developments in deep learning. Notably, we have included the following reference to enrich our discussion:

Meena, G., et al. (2024). "FSTL-SA: Few-Shot Transfer Learning for Sentiment Analysis from Facial Expressions."

3. Embedding Techniques Explanation:

We have elaborated on the embedding techniques utilized in our study, providing a comprehensive explanation of their implementation and role within our methodology.

4. Model Fine-Tuning Details:

Detailed descriptions of the model fine-tuning process have been incorporated, including the specific experiments conducted and the parameters adjusted to optimize performance.

5. Model Parameter Comparison:

We have clarified the comparative analysis of model parameters, outlining the criteria and metrics used to evaluate and contrast the performance of different models.

6. Inclusion of Recent Works and Comparative Analysis:

Our literature review now encompasses major works from 2023 and 2024. We have also included a comparative table that juxtaposes our findings with those of recent studies, including:

Meena, G., et al. (2024). "Monkeypox Recognition and Prediction from Visuals Using Deep Transfer Learning-Based Neural Networks."

7. Dataset Size Specification:

A subsection titled "Dataset Description" has been added to the Methodology section, detailing the size and composition of the dataset used in our study.

8. Proposed Approach Improvements:

We have included a discussion on potential improvements to our proposed approach, highlighting areas for enhancement and refinement.

9. Future Research Directions:

A "Future Work" subsection has been added to outline avenues for future research, building upon the findings of our current study.

10. Challenges in the Work:

We have articulated the challenges encountered during our research, providing a candid discussion of the obstacles faced and how they were addressed.

11. Dataset Challenges:

The "Dataset Description" subsection also includes an analysis of the challenges inherent in our dataset, discussing limitations and potential impacts on our findings.

We trust that these revisions comprehensively address your concerns. Your constructive feedback has been invaluable in strengthening our manuscript, and we are grateful for your thoughtful suggestions.
Warm regards,
Dr. Nachaat Mohamed
Competing Interests: No competing interests were disclosed. Close
Report a concern

Views

Reviewer Report 24 Dec 2024

Haodong Chen, School of Engineering, University of Maryland at College Park, College Park, Maryland, USA

Approved with Reservations

https://doi.org/10.5256/f1000research.175922.r350253

1. Authors should refer to 3-4 related papers to strengthen the review of existing literature and highlight the novelty of this work Suggested papers include:

a. Real-time human-computer interaction using eye gazes. DOI: 10.1016/j.mfglet.2023.07.024
This paper explores a real-time system that uses eye gazes for interacting with computers, which is very relevant to the authors' focus on eye-gesture recognition. By citing this work, the authors can highlight how their AI-driven approach improves accuracy and responsiveness compared to existing gaze-based systems.

b. Real-Time Multi-Modal Human–Robot Collaboration Using Gestures and Speech. DOI: 10.1115/1.4054297
This study looks at combining gestures and speech for better collaboration between humans and robots. Referencing it will help the authors position their eye-gesture control system within the broader field of multi-modal interactions and emphasize the unique advantage of focusing solely on eye movements.

c. Fine-grained activity classification in assembly based on multi-visual modalities. DOI: 10.1007/s10845-023-02152-x
This research dives into detailed activity classification using multiple visual inputs, demonstrating the effectiveness of using diverse visual data. Citing this paper allows the authors to showcase the precision and targeted application of their eye-gesture recognition system, contrasting it with broader multi-visual activity classification methods.

2. Have the authors considered discussing the potential issue of eye strain or fatigue during long-term use of the eye-gesture system? Could they propose possible mitigation strategies, such as adaptive interfaces or periodic calibration breaks?

3. Can the authors provide more detail on the diversity of the dataset, such as specific demographic categories (e.g., age, gender, ethnicity), and explain how this diversity impacts the system's performance across different user groups?

References

1. Chen H, Leu M, Yin Z: Real-Time Multi-Modal Human–Robot Collaboration Using Gestures and Speech. Journal of Manufacturing Science and Engineering. 2022; 144 (10). Publisher Full Text
2. Chen H, Zendehdel N, Leu M, Yin Z: Real-time human-computer interaction using eye gazes. Manufacturing Letters. 2023; 35: 883-894 Publisher Full Text
3. Chen H, Zendehdel N, Leu M, Yin Z: Fine-grained activity classification in assembly based on multi-visual modalities. Journal of Intelligent Manufacturing. 2024; 35 (5): 2215-2233 Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Computer Vision, Artificial Intelligence, AI, Machine Learning, Deep Learning, Human-Computer Interaction (HCI), Eye Gaze Estimation, Gesture Recognition, Industrial AI, generative AI

CITE

Report a concern

Author Response 25 Feb 2025

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

25 Feb 2025

Author Response
Dear Prof. Dr. Haodong Chen,
We sincerely appreciate your valuable feedback and constructive suggestions, which have helped us enhance the clarity, depth, and rigor of our manuscript. Below, we provide ... Continue reading
Dear Prof. Dr. Haodong Chen,
We sincerely appreciate your valuable feedback and constructive suggestions, which have helped us enhance the clarity, depth, and rigor of our manuscript. Below, we provide detailed responses to each of your comments and outline the specific improvements made.
1. Strengthening the Review of Existing Literature
We have revised the Related Work section to incorporate references to the suggested papers. Specifically:

We cited [64] to compare our system with gaze-based human-computer interaction approaches, emphasizing how our method improves accuracy and responsiveness.

Reference [63] has been added to position our work within the broader field of multi-modal interactions, differentiating our approach as a purely eye-gesture-based system.

We also included [65] to contrast our system with multi-visual classification approaches, highlighting our lightweight and real-time solution.

These additions strengthen the theoretical foundation of our study and better contextualize its contributions.
2. Addressing Eye Strain and Fatigue in Long-Term Use
A new subsection titled User Comfort and Long-Term Usability has been added to the Discussion section. This section acknowledges that prolonged use of eye-tracking systems may lead to discomfort or fatigue. To mitigate this, we propose:

Adaptive Sensitivity Adjustments to dynamically modify gesture responsiveness based on detected user fatigue.

Periodic Calibration Breaks to prompt users to rest at optimal intervals.

Customizable Interaction Modes to allow users to personalize gesture sensitivity according to their comfort levels.

These refinements ensure that our system remains comfortable and user-friendly, even during extended usage periods.
3. Expanding Dataset Diversity Details
The Dataset Collection and Composition subsection in the Methodology section has been expanded to include:

Detailed demographic breakdowns of age groups (young adults to seniors), gender (male and female), and ethnic backgrounds to ensure system generalizability.

An analysis of how this diversity impacts system performance, confirming no significant bias across user groups and maintaining high accuracy across all demographics.

A discussion on how our model was tested under varied visual conditions, including participants wearing both reflective and non-reflective glasses.

This ensures that our system is robust, inclusive, and applicable to real-world, diverse user bases.
Conclusion
We appreciate your insightful feedback, which has allowed us to significantly enhance our manuscript. The requested revisions have been implemented, and we believe the updated version now provides a more comprehensive review of existing literature, a stronger dataset description, and a well-justified discussion on long-term usability considerations.
Thank you again for your time and thoughtful comments. We look forward to your feedback on the revised manuscript.
Best regards,
Dr. Nachaat Mohamed
Dear Prof. Dr. Haodong Chen,
We sincerely appreciate your valuable feedback and constructive suggestions, which have helped us enhance the clarity, depth, and rigor of our manuscript. Below, we provide detailed responses to each of your comments and outline the specific improvements made.
1. Strengthening the Review of Existing Literature
We have revised the Related Work section to incorporate references to the suggested papers. Specifically:

We cited [64] to compare our system with gaze-based human-computer interaction approaches, emphasizing how our method improves accuracy and responsiveness.

Reference [63] has been added to position our work within the broader field of multi-modal interactions, differentiating our approach as a purely eye-gesture-based system.

We also included [65] to contrast our system with multi-visual classification approaches, highlighting our lightweight and real-time solution.

These additions strengthen the theoretical foundation of our study and better contextualize its contributions.
2. Addressing Eye Strain and Fatigue in Long-Term Use
A new subsection titled User Comfort and Long-Term Usability has been added to the Discussion section. This section acknowledges that prolonged use of eye-tracking systems may lead to discomfort or fatigue. To mitigate this, we propose:

Adaptive Sensitivity Adjustments to dynamically modify gesture responsiveness based on detected user fatigue.

Periodic Calibration Breaks to prompt users to rest at optimal intervals.

Customizable Interaction Modes to allow users to personalize gesture sensitivity according to their comfort levels.

These refinements ensure that our system remains comfortable and user-friendly, even during extended usage periods.
3. Expanding Dataset Diversity Details
The Dataset Collection and Composition subsection in the Methodology section has been expanded to include:

Detailed demographic breakdowns of age groups (young adults to seniors), gender (male and female), and ethnic backgrounds to ensure system generalizability.

An analysis of how this diversity impacts system performance, confirming no significant bias across user groups and maintaining high accuracy across all demographics.

A discussion on how our model was tested under varied visual conditions, including participants wearing both reflective and non-reflective glasses.

This ensures that our system is robust, inclusive, and applicable to real-world, diverse user bases.
Conclusion
We appreciate your insightful feedback, which has allowed us to significantly enhance our manuscript. The requested revisions have been implemented, and we believe the updated version now provides a more comprehensive review of existing literature, a stronger dataset description, and a well-justified discussion on long-term usability considerations.
Thank you again for your time and thoughtful comments. We look forward to your feedback on the revised manuscript.
Best regards,
Dr. Nachaat Mohamed
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 25 Feb 2025

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

25 Feb 2025

Author Response
Dear Prof. Dr. Haodong Chen,
We sincerely appreciate your valuable feedback and constructive suggestions, which have helped us enhance the clarity, depth, and rigor of our manuscript. Below, we provide ... Continue reading
Dear Prof. Dr. Haodong Chen,
We sincerely appreciate your valuable feedback and constructive suggestions, which have helped us enhance the clarity, depth, and rigor of our manuscript. Below, we provide detailed responses to each of your comments and outline the specific improvements made.
1. Strengthening the Review of Existing Literature
We have revised the Related Work section to incorporate references to the suggested papers. Specifically:

We cited [64] to compare our system with gaze-based human-computer interaction approaches, emphasizing how our method improves accuracy and responsiveness.

Reference [63] has been added to position our work within the broader field of multi-modal interactions, differentiating our approach as a purely eye-gesture-based system.

We also included [65] to contrast our system with multi-visual classification approaches, highlighting our lightweight and real-time solution.

These additions strengthen the theoretical foundation of our study and better contextualize its contributions.
2. Addressing Eye Strain and Fatigue in Long-Term Use
A new subsection titled User Comfort and Long-Term Usability has been added to the Discussion section. This section acknowledges that prolonged use of eye-tracking systems may lead to discomfort or fatigue. To mitigate this, we propose:

Adaptive Sensitivity Adjustments to dynamically modify gesture responsiveness based on detected user fatigue.

Periodic Calibration Breaks to prompt users to rest at optimal intervals.

Customizable Interaction Modes to allow users to personalize gesture sensitivity according to their comfort levels.

These refinements ensure that our system remains comfortable and user-friendly, even during extended usage periods.
3. Expanding Dataset Diversity Details
The Dataset Collection and Composition subsection in the Methodology section has been expanded to include:

Detailed demographic breakdowns of age groups (young adults to seniors), gender (male and female), and ethnic backgrounds to ensure system generalizability.

An analysis of how this diversity impacts system performance, confirming no significant bias across user groups and maintaining high accuracy across all demographics.

A discussion on how our model was tested under varied visual conditions, including participants wearing both reflective and non-reflective glasses.

This ensures that our system is robust, inclusive, and applicable to real-world, diverse user bases.
Conclusion
We appreciate your insightful feedback, which has allowed us to significantly enhance our manuscript. The requested revisions have been implemented, and we believe the updated version now provides a more comprehensive review of existing literature, a stronger dataset description, and a well-justified discussion on long-term usability considerations.
Thank you again for your time and thoughtful comments. We look forward to your feedback on the revised manuscript.
Best regards,
Dr. Nachaat Mohamed
Dear Prof. Dr. Haodong Chen,
We sincerely appreciate your valuable feedback and constructive suggestions, which have helped us enhance the clarity, depth, and rigor of our manuscript. Below, we provide detailed responses to each of your comments and outline the specific improvements made.
1. Strengthening the Review of Existing Literature
We have revised the Related Work section to incorporate references to the suggested papers. Specifically:

We cited [64] to compare our system with gaze-based human-computer interaction approaches, emphasizing how our method improves accuracy and responsiveness.

Reference [63] has been added to position our work within the broader field of multi-modal interactions, differentiating our approach as a purely eye-gesture-based system.

We also included [65] to contrast our system with multi-visual classification approaches, highlighting our lightweight and real-time solution.

These additions strengthen the theoretical foundation of our study and better contextualize its contributions.
2. Addressing Eye Strain and Fatigue in Long-Term Use
A new subsection titled User Comfort and Long-Term Usability has been added to the Discussion section. This section acknowledges that prolonged use of eye-tracking systems may lead to discomfort or fatigue. To mitigate this, we propose:

Adaptive Sensitivity Adjustments to dynamically modify gesture responsiveness based on detected user fatigue.

Periodic Calibration Breaks to prompt users to rest at optimal intervals.

Customizable Interaction Modes to allow users to personalize gesture sensitivity according to their comfort levels.

These refinements ensure that our system remains comfortable and user-friendly, even during extended usage periods.
3. Expanding Dataset Diversity Details
The Dataset Collection and Composition subsection in the Methodology section has been expanded to include:

Detailed demographic breakdowns of age groups (young adults to seniors), gender (male and female), and ethnic backgrounds to ensure system generalizability.

An analysis of how this diversity impacts system performance, confirming no significant bias across user groups and maintaining high accuracy across all demographics.

A discussion on how our model was tested under varied visual conditions, including participants wearing both reflective and non-reflective glasses.

This ensures that our system is robust, inclusive, and applicable to real-world, diverse user bases.
Conclusion
We appreciate your insightful feedback, which has allowed us to significantly enhance our manuscript. The requested revisions have been implemented, and we believe the updated version now provides a more comprehensive review of existing literature, a stronger dataset description, and a well-justified discussion on long-term usability considerations.
Thank you again for your time and thoughtful comments. We look forward to your feedback on the revised manuscript.
Best regards,
Dr. Nachaat Mohamed
Competing Interests: No competing interests were disclosed. Close
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Version 1

VERSION 1

PUBLISHED 19 Feb 2024

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Reviewer Report 03 Sep 2024

Haodong Chen, School of Engineering, University of Maryland at College Park, College Park, Maryland, USA

Approved with Reservations

https://doi.org/10.5256/f1000research.158833.r285544

Summary of the Article:
The paper introduces an advanced AI-based system that utilizes eye gestures to control computer systems, achieving an impressive accuracy of over 99.63%. This system leverages widely used libraries such as OpenCV, mediapipe, and pyautogui, making it accessible for implementation and demonstrating significant potential in applications ranging from accessibility enhancements for individuals with physical impairments to intuitive interaction in various fields, including military and rescue operations.
Major Points:

Long-Term Usability and Comfort: The proposed eye gaze system presents a highly accurate and innovative approach. However, one of the key aspects that the paper does not address is the long-term usability and comfort of such eye gesture systems. Prolonged use could lead to eye strain or fatigue, which may diminish the system's practicality over time. Including an analysis or discussion on this aspect would strengthen the paper significantly.
Privacy Concerns: Another major consideration is the potential privacy implications associated with continuous eye tracking. The paper does not discuss how the system handles or safeguards the sensitive data it collects from users. Addressing these concerns is crucial, especially for systems that may be deployed in sensitive environments or for vulnerable populations.
Comparison with Existing Technologies: The paper lacks a thorough comparison with existing eye-gesture control technologies. While the accuracy of the proposed system is commendable, readers would benefit from understanding how this system stacks up against other similar solutions in terms of performance, usability, and practicality. A comparative analysis would provide valuable context for evaluating the system's novelty and effectiveness.
Face Landmark Detection Method: The method employed for face landmark detection is not original to the authors and relies on existing algorithms. If the authors choose to apply established methods, it would be beneficial to include more detailed design elements and interface considerations specific to this application. For further insights and ideas, the authors may refer to the following works:
- Chen, H, et.al., 2023 (Ref 1): Chen, H., Zendehdel, N., Leu, M.C. and Yin, Z., 2023. Real-time human-computer interaction using eye gazes. Manufacturing Letters, 35, pp.883-894.
- Chen, H, et.al., 2022 (Ref 2): Chen, H., Zendehdel, N., Leu, M.C. and Yin, Z., 2022. Multi-Modal Fine-Grained Activity Recognition and Prediction in Assembly. Research Square Platform LLC.
- Chen H, et.al., 2024 (Ref 3): Chen, H., Zendehdel, N., Leu, M.C. and Yin, Z., 2024. Fine-grained activity classification in assembly based on multi-visual modalities. Journal of Intelligent Manufacturing, 35(5), pp.2215-2233.

Minor Points:

Dataset Diversity: The paper provides limited information on the diversity of the dataset used for testing the system. Given the importance of generalizability in AI systems, it is essential to ensure that the dataset is representative of various demographics and usage conditions. Please Include more details on the dataset's composition and how it impacts the system's performance across different scenarios.

Strengths:

The system's high accuracy rate of over 99.63% is a significant achievement, highlighting its potential for practical applications.
The use of accessible and widely recognized libraries such as OpenCV, mediapipe, and pyautogui is a commendable choice, making the technology easier to replicate and adapt by others.
The potential applications of this technology, particularly in aiding individuals with physical impairments and in critical fields like military and rescue operations, are well-justified and promising.

Overall Evaluation:
The study design is generally appropriate, and the work is technically sound. However, the paper could benefit from a more in-depth discussion of long-term usability, privacy concerns, and a comparison with existing technologies. Sufficient details of the methods are provided to allow replication, though additional information on dataset diversity would be advantageous. The conclusions are well-supported by the results, but the inclusion of the aforementioned points would significantly strengthen the paper.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Chen H, Zendehdel N, Leu M, Yin Z: Real-time human-computer interaction using eye gazes. Manufacturing Letters. 2023; 35: 883-894 Publisher Full Text
2. Chen H, Leu M, Yin Z: Real-Time Multi-Modal Human–Robot Collaboration Using Gestures and Speech. Journal of Manufacturing Science and Engineering. 2022; 144 (10). Publisher Full Text
3. Chen H, Zendehdel N, Leu M, Yin Z: Fine-grained activity classification in assembly based on multi-visual modalities. Journal of Intelligent Manufacturing. 2024; 35 (5): 2215-2233 Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Computer Vision, Artificial Intelligence, AI, Machine Learning, Deep Learning, Human-Computer Interaction (HCI), Eye Gaze Estimation, Gesture Recognition

CITE

Report a concern

Author Response 22 Oct 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

22 Oct 2024

Author Response

Thanks a lot Dr. Haodong Chen
We will address this concern by discussing the potential for eye strain and the need for breaks during prolonged use. We will also suggest ... Continue reading Thanks a lot Dr. Haodong Chen
We will address this concern by discussing the potential for eye strain and the need for breaks during prolonged use. We will also suggest future enhancements that could mitigate these issues.
We will address privacy concerns by explaining how data is anonymized and secured, in compliance with relevant data protection regulations.
We will add a comparative analysis of existing eye-gesture control technologies, highlighting the differences in accuracy, ease of implementation, and other performance metrics.
We will acknowledge the use of established face landmark detection algorithms and explain the specific adaptations made for our system to improve performance.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.
Thanks a lot Dr. Haodong Chen
We will address this concern by discussing the potential for eye strain and the need for breaks during prolonged use. We will also suggest future enhancements that could mitigate these issues.
We will address privacy concerns by explaining how data is anonymized and secured, in compliance with relevant data protection regulations.
We will add a comparative analysis of existing eye-gesture control technologies, highlighting the differences in accuracy, ease of implementation, and other performance metrics.
We will acknowledge the use of established face landmark detection algorithms and explain the specific adaptations made for our system to improve performance.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Author Response 18 Dec 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

18 Dec 2024

Author Response

Thank you for this valuable observation. We have added a discussion on the long-term usability and potential comfort issues associated with prolonged use of the eye-gesture system. Specifically, we addressed ... Continue reading Thank you for this valuable observation. We have added a discussion on the long-term usability and potential comfort issues associated with prolonged use of the eye-gesture system. Specifically, we addressed concerns about eye strain and proposed potential mitigation strategies, such as incorporating adaptive interface designs and periodic usage breaks. These additions can be found in the revised Discussion section.

We appreciate this important suggestion. To address this, we have included a detailed discussion on privacy and data protection measures in the Discussion section. The revised text highlights our strict data anonymization protocols, encryption methods, and adherence to international privacy standards, such as GDPR. These measures ensure that user privacy is safeguarded during data collection and processing.

Thank you for pointing this out. We have expanded the Related Work section to include a comparison with existing eye-gesture control technologies. The updated section highlights the advantages of our system, including its higher accuracy (99.63%) compared to reported ranges (95%–99%), and its reliance on accessible tools like OpenCV and PyAutoGUI, which eliminate the need for specialized hardware. This comparison emphasizes the practical and cost-effective nature of our approach.

We acknowledge this comment and have clarified in the Methodology section how our system builds upon existing face landmark detection algorithms (e.g., MediaPipe) by incorporating customized calibration techniques. These adaptations optimize the system’s performance for real-time applications and diverse user conditions, addressing specific challenges such as varying facial structures and dynamic lighting environments. The revisions outline the unique contributions of these enhancements.

Thank you for highlighting this. We have revised the Methodology section to provide detailed information about the dataset. It comprises 20,000 gesture instances collected from 100 volunteers representing diverse demographics, including variations in age, gender, and ethnicity. The diversity of the dataset was intentionally designed to ensure the system's generalizability and robustness in real-world applications.
Thank you for this valuable observation. We have added a discussion on the long-term usability and potential comfort issues associated with prolonged use of the eye-gesture system. Specifically, we addressed concerns about eye strain and proposed potential mitigation strategies, such as incorporating adaptive interface designs and periodic usage breaks. These additions can be found in the revised Discussion section.

We appreciate this important suggestion. To address this, we have included a detailed discussion on privacy and data protection measures in the Discussion section. The revised text highlights our strict data anonymization protocols, encryption methods, and adherence to international privacy standards, such as GDPR. These measures ensure that user privacy is safeguarded during data collection and processing.

Thank you for pointing this out. We have expanded the Related Work section to include a comparison with existing eye-gesture control technologies. The updated section highlights the advantages of our system, including its higher accuracy (99.63%) compared to reported ranges (95%–99%), and its reliance on accessible tools like OpenCV and PyAutoGUI, which eliminate the need for specialized hardware. This comparison emphasizes the practical and cost-effective nature of our approach.

We acknowledge this comment and have clarified in the Methodology section how our system builds upon existing face landmark detection algorithms (e.g., MediaPipe) by incorporating customized calibration techniques. These adaptations optimize the system’s performance for real-time applications and diverse user conditions, addressing specific challenges such as varying facial structures and dynamic lighting environments. The revisions outline the unique contributions of these enhancements.

Thank you for highlighting this. We have revised the Methodology section to provide detailed information about the dataset. It comprises 20,000 gesture instances collected from 100 volunteers representing diverse demographics, including variations in age, gender, and ethnicity. The diversity of the dataset was intentionally designed to ensure the system's generalizability and robustness in real-world applications.
Competing Interests: No competing interests were disclosed. Close
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Respond or Comment

COMMENTS ON THIS REPORT

Author Response 22 Oct 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

22 Oct 2024

Author Response

Thanks a lot Dr. Haodong Chen
We will address this concern by discussing the potential for eye strain and the need for breaks during prolonged use. We will also suggest ... Continue reading Thanks a lot Dr. Haodong Chen
We will address this concern by discussing the potential for eye strain and the need for breaks during prolonged use. We will also suggest future enhancements that could mitigate these issues.
We will address privacy concerns by explaining how data is anonymized and secured, in compliance with relevant data protection regulations.
We will add a comparative analysis of existing eye-gesture control technologies, highlighting the differences in accuracy, ease of implementation, and other performance metrics.
We will acknowledge the use of established face landmark detection algorithms and explain the specific adaptations made for our system to improve performance.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.
Thanks a lot Dr. Haodong Chen
We will address this concern by discussing the potential for eye strain and the need for breaks during prolonged use. We will also suggest future enhancements that could mitigate these issues.
We will address privacy concerns by explaining how data is anonymized and secured, in compliance with relevant data protection regulations.
We will add a comparative analysis of existing eye-gesture control technologies, highlighting the differences in accuracy, ease of implementation, and other performance metrics.
We will acknowledge the use of established face landmark detection algorithms and explain the specific adaptations made for our system to improve performance.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Author Response 18 Dec 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

18 Dec 2024

Author Response

Thank you for this valuable observation. We have added a discussion on the long-term usability and potential comfort issues associated with prolonged use of the eye-gesture system. Specifically, we addressed ... Continue reading Thank you for this valuable observation. We have added a discussion on the long-term usability and potential comfort issues associated with prolonged use of the eye-gesture system. Specifically, we addressed concerns about eye strain and proposed potential mitigation strategies, such as incorporating adaptive interface designs and periodic usage breaks. These additions can be found in the revised Discussion section.

We appreciate this important suggestion. To address this, we have included a detailed discussion on privacy and data protection measures in the Discussion section. The revised text highlights our strict data anonymization protocols, encryption methods, and adherence to international privacy standards, such as GDPR. These measures ensure that user privacy is safeguarded during data collection and processing.

Thank you for pointing this out. We have expanded the Related Work section to include a comparison with existing eye-gesture control technologies. The updated section highlights the advantages of our system, including its higher accuracy (99.63%) compared to reported ranges (95%–99%), and its reliance on accessible tools like OpenCV and PyAutoGUI, which eliminate the need for specialized hardware. This comparison emphasizes the practical and cost-effective nature of our approach.

We acknowledge this comment and have clarified in the Methodology section how our system builds upon existing face landmark detection algorithms (e.g., MediaPipe) by incorporating customized calibration techniques. These adaptations optimize the system’s performance for real-time applications and diverse user conditions, addressing specific challenges such as varying facial structures and dynamic lighting environments. The revisions outline the unique contributions of these enhancements.

Thank you for highlighting this. We have revised the Methodology section to provide detailed information about the dataset. It comprises 20,000 gesture instances collected from 100 volunteers representing diverse demographics, including variations in age, gender, and ethnicity. The diversity of the dataset was intentionally designed to ensure the system's generalizability and robustness in real-world applications.
Thank you for this valuable observation. We have added a discussion on the long-term usability and potential comfort issues associated with prolonged use of the eye-gesture system. Specifically, we addressed concerns about eye strain and proposed potential mitigation strategies, such as incorporating adaptive interface designs and periodic usage breaks. These additions can be found in the revised Discussion section.

We appreciate this important suggestion. To address this, we have included a detailed discussion on privacy and data protection measures in the Discussion section. The revised text highlights our strict data anonymization protocols, encryption methods, and adherence to international privacy standards, such as GDPR. These measures ensure that user privacy is safeguarded during data collection and processing.

Thank you for pointing this out. We have expanded the Related Work section to include a comparison with existing eye-gesture control technologies. The updated section highlights the advantages of our system, including its higher accuracy (99.63%) compared to reported ranges (95%–99%), and its reliance on accessible tools like OpenCV and PyAutoGUI, which eliminate the need for specialized hardware. This comparison emphasizes the practical and cost-effective nature of our approach.

We acknowledge this comment and have clarified in the Methodology section how our system builds upon existing face landmark detection algorithms (e.g., MediaPipe) by incorporating customized calibration techniques. These adaptations optimize the system’s performance for real-time applications and diverse user conditions, addressing specific challenges such as varying facial structures and dynamic lighting environments. The revisions outline the unique contributions of these enhancements.

Thank you for highlighting this. We have revised the Methodology section to provide detailed information about the dataset. It comprises 20,000 gesture instances collected from 100 volunteers representing diverse demographics, including variations in age, gender, and ethnicity. The diversity of the dataset was intentionally designed to ensure the system's generalizability and robustness in real-world applications.
Competing Interests: No competing interests were disclosed. Close
Report a concern

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Reviewer Report 31 Jul 2024

Zakariyya Abdullahi Bature, King Mongkut's University of Technology Thonburi, Bangkok, Thailand

Approved with Reservations

https://doi.org/10.5256/f1000research.158833.r285546

This paper demonstrated AI-based eye-gesture recognition for computer system control. The author tries to capture the raw eye movement and translates it into human action using an AI-based algorithm. As it stands, I have a few concerns/questions to recommend this paper for indexed.

1. In the abstract and the method, this work described the tools used but partially described the algorithms, and how they solve the problems compared to the previous works. Moreover, it does not accurately describe the exact problems of the existing works. How the proposed method captures and converts the eye movement into actions.
2. The details of the dataset are poorly explained. Even though the author has provided the link to the dataset, but is poorly explained and labeled. Details such as the number of participants in the dataset contained, number of the gestures, repetitions, and frame numbers are needed to assess the robustness of the algorithms.
3. Details of the presentation of the results are needed in terms of the number of gestures. Please use some of the visualizing tools such as the confusion matrix, bar chart, etc. to visualize the recognition performance of your proposed method on each eye gesture.
4. It is very difficult to conclude the robustness of your method from the results of a single dataset. Please, use publicly available datasets to validate your proposed method and compare the results of the datasets with state-of-the-art methods.
5. Please state the drawback of your proposed method in the conclusion.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Machine Learning with interests in Computer Vision, Digital Image Processing, Feature Selection Methods, Optimization with their application to Eye Tracking, Human Gesture Recognition, Signal Processing, Biometrics Identification and related topics

CITE

Report a concern

Author Response 23 Oct 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

23 Oct 2024

Author Response

Thanks a lot Dr. Zakariyya Abdullahi
We will enhance the explanation of the algorithm, comparing it with prior works and highlighting how our system addresses the gaps. This will clearly ... Continue reading Thanks a lot Dr. Zakariyya Abdullahi
We will enhance the explanation of the algorithm, comparing it with prior works and highlighting how our system addresses the gaps. This will clearly detail how the eye gestures are translated into commands, emphasizing the novelty of our approach.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.
We will include visual Confusion Matrix at the end of the results section, to better illustrate the system's performance and its effectiveness across various gestures.
We will clarify that the dataset used in this study was collected from 100 volunteers, ensuring a diverse representation in terms of age, gender, and ethnicity. This dataset, comprising 20,000 gesture instances, was instrumental in achieving a high accuracy rate of 99.63%. However, we acknowledge the importance of further validating the system using publicly available datasets to enhance generalizability. We are currently exploring the integration of external datasets, such as those from Dryad, to perform comparative analysis and further assess the robustness of our model in different scenarios.
We will explicitly state the potential limitations of the method, including the challenges of using the system in low-light conditions and potential issues with glasses or contact lenses.
Thanks a lot Dr. Zakariyya Abdullahi
We will enhance the explanation of the algorithm, comparing it with prior works and highlighting how our system addresses the gaps. This will clearly detail how the eye gestures are translated into commands, emphasizing the novelty of our approach.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.
We will include visual Confusion Matrix at the end of the results section, to better illustrate the system's performance and its effectiveness across various gestures.
We will clarify that the dataset used in this study was collected from 100 volunteers, ensuring a diverse representation in terms of age, gender, and ethnicity. This dataset, comprising 20,000 gesture instances, was instrumental in achieving a high accuracy rate of 99.63%. However, we acknowledge the importance of further validating the system using publicly available datasets to enhance generalizability. We are currently exploring the integration of external datasets, such as those from Dryad, to perform comparative analysis and further assess the robustness of our model in different scenarios.
We will explicitly state the potential limitations of the method, including the challenges of using the system in low-light conditions and potential issues with glasses or contact lenses.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Author Response 18 Dec 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

18 Dec 2024

Author Response

Thank you for highlighting this point. We have revised the manuscript to provide a detailed description of the algorithm. The updated text includes an explanation of how the system uses ... Continue reading Thank you for highlighting this point. We have revised the manuscript to provide a detailed description of the algorithm. The updated text includes an explanation of how the system uses machine learning models integrated with OpenCV and PyAutoGUI to capture eye movements and translate them into actionable commands. Additionally, we have added a comparison with prior works, emphasizing the improvements in speed, accuracy, and generalizability. These changes can be found in the revised Methodology and Related Work sections.

We appreciate this observation and have updated the manuscript to include comprehensive details about the dataset. Specifically, the dataset comprises 20,000 gesture instances collected from 100 volunteers, each representing a diverse range of demographics. Each participant performed 10 distinct gestures, repeated 20 times. These details, along with the methodology for dataset collection and preprocessing, are now clearly outlined in the Methodology section.

Thank you for this suggestion. We have incorporated visual representations, including a confusion matrix and bar charts, to present the system's performance across the 10 distinct gestures. These additions are now included in the Results section, providing a clearer understanding of classification accuracy, potential misclassifications, and overall performance metrics.

We acknowledge the importance of validating the system with publicly available datasets. While the primary dataset used in this study was collected from 100 volunteers to ensure diversity and robustness, we are actively exploring external datasets, such as those from Dryad, for further validation and comparison with other state-of-the-art methods. This effort will be part of our future work and has been discussed in the revised Results and Conclusion sections.

Thank you for this recommendation. We have updated the Conclusion section to explicitly discuss the system's limitations. These include minor accuracy reductions in scenarios involving reflective glasses (98.9%) and the need for further validation with publicly available datasets to enhance generalizability. We believe this addition provides a more balanced and complete assessment of the system.
Thank you for highlighting this point. We have revised the manuscript to provide a detailed description of the algorithm. The updated text includes an explanation of how the system uses machine learning models integrated with OpenCV and PyAutoGUI to capture eye movements and translate them into actionable commands. Additionally, we have added a comparison with prior works, emphasizing the improvements in speed, accuracy, and generalizability. These changes can be found in the revised Methodology and Related Work sections.

We appreciate this observation and have updated the manuscript to include comprehensive details about the dataset. Specifically, the dataset comprises 20,000 gesture instances collected from 100 volunteers, each representing a diverse range of demographics. Each participant performed 10 distinct gestures, repeated 20 times. These details, along with the methodology for dataset collection and preprocessing, are now clearly outlined in the Methodology section.

Thank you for this suggestion. We have incorporated visual representations, including a confusion matrix and bar charts, to present the system's performance across the 10 distinct gestures. These additions are now included in the Results section, providing a clearer understanding of classification accuracy, potential misclassifications, and overall performance metrics.

We acknowledge the importance of validating the system with publicly available datasets. While the primary dataset used in this study was collected from 100 volunteers to ensure diversity and robustness, we are actively exploring external datasets, such as those from Dryad, for further validation and comparison with other state-of-the-art methods. This effort will be part of our future work and has been discussed in the revised Results and Conclusion sections.

Thank you for this recommendation. We have updated the Conclusion section to explicitly discuss the system's limitations. These include minor accuracy reductions in scenarios involving reflective glasses (98.9%) and the need for further validation with publicly available datasets to enhance generalizability. We believe this addition provides a more balanced and complete assessment of the system.
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 23 Oct 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

23 Oct 2024

Author Response

Thanks a lot Dr. Zakariyya Abdullahi
We will enhance the explanation of the algorithm, comparing it with prior works and highlighting how our system addresses the gaps. This will clearly ... Continue reading Thanks a lot Dr. Zakariyya Abdullahi
We will enhance the explanation of the algorithm, comparing it with prior works and highlighting how our system addresses the gaps. This will clearly detail how the eye gestures are translated into commands, emphasizing the novelty of our approach.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.
We will include visual Confusion Matrix at the end of the results section, to better illustrate the system's performance and its effectiveness across various gestures.
We will clarify that the dataset used in this study was collected from 100 volunteers, ensuring a diverse representation in terms of age, gender, and ethnicity. This dataset, comprising 20,000 gesture instances, was instrumental in achieving a high accuracy rate of 99.63%. However, we acknowledge the importance of further validating the system using publicly available datasets to enhance generalizability. We are currently exploring the integration of external datasets, such as those from Dryad, to perform comparative analysis and further assess the robustness of our model in different scenarios.
We will explicitly state the potential limitations of the method, including the challenges of using the system in low-light conditions and potential issues with glasses or contact lenses.
Thanks a lot Dr. Zakariyya Abdullahi
We will enhance the explanation of the algorithm, comparing it with prior works and highlighting how our system addresses the gaps. This will clearly detail how the eye gestures are translated into commands, emphasizing the novelty of our approach.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.
We will include visual Confusion Matrix at the end of the results section, to better illustrate the system's performance and its effectiveness across various gestures.
We will clarify that the dataset used in this study was collected from 100 volunteers, ensuring a diverse representation in terms of age, gender, and ethnicity. This dataset, comprising 20,000 gesture instances, was instrumental in achieving a high accuracy rate of 99.63%. However, we acknowledge the importance of further validating the system using publicly available datasets to enhance generalizability. We are currently exploring the integration of external datasets, such as those from Dryad, to perform comparative analysis and further assess the robustness of our model in different scenarios.
We will explicitly state the potential limitations of the method, including the challenges of using the system in low-light conditions and potential issues with glasses or contact lenses.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Author Response 18 Dec 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

18 Dec 2024

Author Response

Thank you for highlighting this point. We have revised the manuscript to provide a detailed description of the algorithm. The updated text includes an explanation of how the system uses ... Continue reading Thank you for highlighting this point. We have revised the manuscript to provide a detailed description of the algorithm. The updated text includes an explanation of how the system uses machine learning models integrated with OpenCV and PyAutoGUI to capture eye movements and translate them into actionable commands. Additionally, we have added a comparison with prior works, emphasizing the improvements in speed, accuracy, and generalizability. These changes can be found in the revised Methodology and Related Work sections.

We appreciate this observation and have updated the manuscript to include comprehensive details about the dataset. Specifically, the dataset comprises 20,000 gesture instances collected from 100 volunteers, each representing a diverse range of demographics. Each participant performed 10 distinct gestures, repeated 20 times. These details, along with the methodology for dataset collection and preprocessing, are now clearly outlined in the Methodology section.

Thank you for this suggestion. We have incorporated visual representations, including a confusion matrix and bar charts, to present the system's performance across the 10 distinct gestures. These additions are now included in the Results section, providing a clearer understanding of classification accuracy, potential misclassifications, and overall performance metrics.

We acknowledge the importance of validating the system with publicly available datasets. While the primary dataset used in this study was collected from 100 volunteers to ensure diversity and robustness, we are actively exploring external datasets, such as those from Dryad, for further validation and comparison with other state-of-the-art methods. This effort will be part of our future work and has been discussed in the revised Results and Conclusion sections.

Thank you for this recommendation. We have updated the Conclusion section to explicitly discuss the system's limitations. These include minor accuracy reductions in scenarios involving reflective glasses (98.9%) and the need for further validation with publicly available datasets to enhance generalizability. We believe this addition provides a more balanced and complete assessment of the system.
Thank you for highlighting this point. We have revised the manuscript to provide a detailed description of the algorithm. The updated text includes an explanation of how the system uses machine learning models integrated with OpenCV and PyAutoGUI to capture eye movements and translate them into actionable commands. Additionally, we have added a comparison with prior works, emphasizing the improvements in speed, accuracy, and generalizability. These changes can be found in the revised Methodology and Related Work sections.

We appreciate this observation and have updated the manuscript to include comprehensive details about the dataset. Specifically, the dataset comprises 20,000 gesture instances collected from 100 volunteers, each representing a diverse range of demographics. Each participant performed 10 distinct gestures, repeated 20 times. These details, along with the methodology for dataset collection and preprocessing, are now clearly outlined in the Methodology section.

Thank you for this suggestion. We have incorporated visual representations, including a confusion matrix and bar charts, to present the system's performance across the 10 distinct gestures. These additions are now included in the Results section, providing a clearer understanding of classification accuracy, potential misclassifications, and overall performance metrics.

We acknowledge the importance of validating the system with publicly available datasets. While the primary dataset used in this study was collected from 100 volunteers to ensure diversity and robustness, we are actively exploring external datasets, such as those from Dryad, for further validation and comparison with other state-of-the-art methods. This effort will be part of our future work and has been discussed in the revised Results and Conclusion sections.

Thank you for this recommendation. We have updated the Conclusion section to explicitly discuss the system's limitations. These include minor accuracy reductions in scenarios involving reflective glasses (98.9%) and the need for further validation with publicly available datasets to enhance generalizability. We believe this addition provides a more balanced and complete assessment of the system.
Competing Interests: No competing interests were disclosed. Close
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Version 3

VERSION 3 PUBLISHED 19 Feb 2024

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Zakariyya Abdullahi Bature, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
Haodong Chen, University of Maryland at College Park, College Park, USA
Gaurav Meena, Central University of Rajasthan, Ajmer, India

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03 Mar 2025 | for Version 3

Gaurav Meena, Central University of Rajasthan, Ajmer, India

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Approved

Accepted. The authors incorporated all the changes.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Deep Learning, Artificial Intelligence

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report

11 Views

24 Jan 2025 | for Version 2

Gaurav Meena, Central University of Rajasthan, Ajmer, India

11 Views Cite this report Responses(1)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Deep Learning, Artificial Intelligence

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Responses (1)

Author Response

25 Feb 2025

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

Dear Dr. Meena,
We sincerely appreciate your thorough review and valuable feedback on our manuscript. Your insights have been instrumental in enhancing the quality and clarity of our work. Below, we address each of your comments in detail:
1. Introduction Enhancements:

Objectives and Motivations: We have expanded the introduction to include a detailed discussion of the objectives and motivations of our study, explicitly linking them to identified gaps in the existing literature.
Research Questions: A new subsection outlining the specific research questions guiding our investigation has been added to provide clarity on the study's focus.

2. Comprehensive Literature Review:

We have conducted an extensive review of recent literature, particularly focusing on state-of-the-art developments in deep learning. Notably, we have included the following reference to enrich our discussion:
- Meena, G., et al. (2024). "FSTL-SA: Few-Shot Transfer Learning for Sentiment Analysis from Facial Expressions."

3. Embedding Techniques Explanation:

We have elaborated on the embedding techniques utilized in our study, providing a comprehensive explanation of their implementation and role within our methodology.

4. Model Fine-Tuning Details:

Detailed descriptions of the model fine-tuning process have been incorporated, including the specific experiments conducted and the parameters adjusted to optimize performance.

5. Model Parameter Comparison:

We have clarified the comparative analysis of model parameters, outlining the criteria and metrics used to evaluate and contrast the performance of different models.

6. Inclusion of Recent Works and Comparative Analysis:

Our literature review now encompasses major works from 2023 and 2024. We have also included a comparative table that juxtaposes our findings with those of recent studies, including:
- Meena, G., et al. (2024). "Monkeypox Recognition and Prediction from Visuals Using Deep Transfer Learning-Based Neural Networks."

7. Dataset Size Specification:

A subsection titled "Dataset Description" has been added to the Methodology section, detailing the size and composition of the dataset used in our study.

8. Proposed Approach Improvements:

We have included a discussion on potential improvements to our proposed approach, highlighting areas for enhancement and refinement.

9. Future Research Directions:

A "Future Work" subsection has been added to outline avenues for future research, building upon the findings of our current study.

10. Challenges in the Work:

We have articulated the challenges encountered during our research, providing a candid discussion of the obstacles faced and how they were addressed.

11. Dataset Challenges:

The "Dataset Description" subsection also includes an analysis of the challenges inherent in our dataset, discussing limitations and potential impacts on our findings.

We trust that these revisions comprehensively address your concerns. Your constructive feedback has been invaluable in strengthening our manuscript, and we are grateful for your thoughtful suggestions.
Warm regards,
Dr. Nachaat Mohamed

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

13 Views

24 Dec 2024 | for Version 2

Haodong Chen, School of Engineering, University of Maryland at College Park, College Park, Maryland, USA

13 Views Cite this report Responses(1)

Approved With Reservations

References

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Computer Vision, Artificial Intelligence, AI, Machine Learning, Deep Learning, Human-Computer Interaction (HCI), Eye Gaze Estimation, Gesture Recognition, Industrial AI, generative AI

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Responses (1)

Author Response

25 Feb 2025

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

Dear Prof. Dr. Haodong Chen,
We sincerely appreciate your valuable feedback and constructive suggestions, which have helped us enhance the clarity, depth, and rigor of our manuscript. Below, we provide detailed responses to each of your comments and outline the specific improvements made.
1. Strengthening the Review of Existing Literature
We have revised the Related Work section to incorporate references to the suggested papers. Specifically:

We cited [64] to compare our system with gaze-based human-computer interaction approaches, emphasizing how our method improves accuracy and responsiveness.
Reference [63] has been added to position our work within the broader field of multi-modal interactions, differentiating our approach as a purely eye-gesture-based system.
We also included [65] to contrast our system with multi-visual classification approaches, highlighting our lightweight and real-time solution.

These additions strengthen the theoretical foundation of our study and better contextualize its contributions.
2. Addressing Eye Strain and Fatigue in Long-Term Use
A new subsection titled User Comfort and Long-Term Usability has been added to the Discussion section. This section acknowledges that prolonged use of eye-tracking systems may lead to discomfort or fatigue. To mitigate this, we propose:

Adaptive Sensitivity Adjustments to dynamically modify gesture responsiveness based on detected user fatigue.
Periodic Calibration Breaks to prompt users to rest at optimal intervals.
Customizable Interaction Modes to allow users to personalize gesture sensitivity according to their comfort levels.

These refinements ensure that our system remains comfortable and user-friendly, even during extended usage periods.
3. Expanding Dataset Diversity Details
The Dataset Collection and Composition subsection in the Methodology section has been expanded to include:

Detailed demographic breakdowns of age groups (young adults to seniors), gender (male and female), and ethnic backgrounds to ensure system generalizability.
An analysis of how this diversity impacts system performance, confirming no significant bias across user groups and maintaining high accuracy across all demographics.
A discussion on how our model was tested under varied visual conditions, including participants wearing both reflective and non-reflective glasses.

This ensures that our system is robust, inclusive, and applicable to real-world, diverse user bases.
Conclusion
We appreciate your insightful feedback, which has allowed us to significantly enhance our manuscript. The requested revisions have been implemented, and we believe the updated version now provides a more comprehensive review of existing literature, a stronger dataset description, and a well-justified discussion on long-term usability considerations.
Thank you again for your time and thoughtful comments. We look forward to your feedback on the revised manuscript.
Best regards,
Dr. Nachaat Mohamed

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

16 Views

03 Sep 2024 | for Version 1

Haodong Chen, School of Engineering, University of Maryland at College Park, College Park, Maryland, USA

16 Views Cite this report Responses(2)

Approved With Reservations

Long-Term Usability and Comfort: The proposed eye gaze system presents a highly accurate and innovative approach. However, one of the key aspects that the paper does not address is the long-term usability and comfort of such eye gesture systems. Prolonged use could lead to eye strain or fatigue, which may diminish the system's practicality over time. Including an analysis or discussion on this aspect would strengthen the paper significantly.
Privacy Concerns: Another major consideration is the potential privacy implications associated with continuous eye tracking. The paper does not discuss how the system handles or safeguards the sensitive data it collects from users. Addressing these concerns is crucial, especially for systems that may be deployed in sensitive environments or for vulnerable populations.
Comparison with Existing Technologies: The paper lacks a thorough comparison with existing eye-gesture control technologies. While the accuracy of the proposed system is commendable, readers would benefit from understanding how this system stacks up against other similar solutions in terms of performance, usability, and practicality. A comparative analysis would provide valuable context for evaluating the system's novelty and effectiveness.
Face Landmark Detection Method: The method employed for face landmark detection is not original to the authors and relies on existing algorithms. If the authors choose to apply established methods, it would be beneficial to include more detailed design elements and interface considerations specific to this application. For further insights and ideas, the authors may refer to the following works:
- Chen, H, et.al., 2023 (Ref 1): Chen, H., Zendehdel, N., Leu, M.C. and Yin, Z., 2023. Real-time human-computer interaction using eye gazes. Manufacturing Letters, 35, pp.883-894.
- Chen, H, et.al., 2022 (Ref 2): Chen, H., Zendehdel, N., Leu, M.C. and Yin, Z., 2022. Multi-Modal Fine-Grained Activity Recognition and Prediction in Assembly. Research Square Platform LLC.
- Chen H, et.al., 2024 (Ref 3): Chen, H., Zendehdel, N., Leu, M.C. and Yin, Z., 2024. Fine-grained activity classification in assembly based on multi-visual modalities. Journal of Intelligent Manufacturing, 35(5), pp.2215-2233.

Minor Points:

Dataset Diversity: The paper provides limited information on the diversity of the dataset used for testing the system. Given the importance of generalizability in AI systems, it is essential to ensure that the dataset is representative of various demographics and usage conditions. Please Include more details on the dataset's composition and how it impacts the system's performance across different scenarios.

Strengths:

The system's high accuracy rate of over 99.63% is a significant achievement, highlighting its potential for practical applications.
The use of accessible and widely recognized libraries such as OpenCV, mediapipe, and pyautogui is a commendable choice, making the technology easier to replicate and adapt by others.
The potential applications of this technology, particularly in aiding individuals with physical impairments and in critical fields like military and rescue operations, are well-justified and promising.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Computer Vision, Artificial Intelligence, AI, Machine Learning, Deep Learning, Human-Computer Interaction (HCI), Eye Gaze Estimation, Gesture Recognition

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Author Response

22 Oct 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

Thanks a lot Dr. Haodong Chen
We will address this concern by discussing the potential for eye strain and the need for breaks during prolonged use. We will also suggest future enhancements that could mitigate these issues.
We will address privacy concerns by explaining how data is anonymized and secured, in compliance with relevant data protection regulations.
We will add a comparative analysis of existing eye-gesture control technologies, highlighting the differences in accuracy, ease of implementation, and other performance metrics.
We will acknowledge the use of established face landmark detection algorithms and explain the specific adaptations made for our system to improve performance.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.

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Competing Interests

No competing interests were disclosed.

Author Response

18 Dec 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

Thank you for this valuable observation. We have added a discussion on the long-term usability and potential comfort issues associated with prolonged use of the eye-gesture system. Specifically, we addressed concerns about eye strain and proposed potential mitigation strategies, such as incorporating adaptive interface designs and periodic usage breaks. These additions can be found in the revised Discussion section.

We appreciate this important suggestion. To address this, we have included a detailed discussion on privacy and data protection measures in the Discussion section. The revised text highlights our strict data anonymization protocols, encryption methods, and adherence to international privacy standards, such as GDPR. These measures ensure that user privacy is safeguarded during data collection and processing.

Thank you for pointing this out. We have expanded the Related Work section to include a comparison with existing eye-gesture control technologies. The updated section highlights the advantages of our system, including its higher accuracy (99.63%) compared to reported ranges (95%–99%), and its reliance on accessible tools like OpenCV and PyAutoGUI, which eliminate the need for specialized hardware. This comparison emphasizes the practical and cost-effective nature of our approach.

We acknowledge this comment and have clarified in the Methodology section how our system builds upon existing face landmark detection algorithms (e.g., MediaPipe) by incorporating customized calibration techniques. These adaptations optimize the system’s performance for real-time applications and diverse user conditions, addressing specific challenges such as varying facial structures and dynamic lighting environments. The revisions outline the unique contributions of these enhancements.

Thank you for highlighting this. We have revised the Methodology section to provide detailed information about the dataset. It comprises 20,000 gesture instances collected from 100 volunteers representing diverse demographics, including variations in age, gender, and ethnicity. The diversity of the dataset was intentionally designed to ensure the system's generalizability and robustness in real-world applications.

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

19 Views

31 Jul 2024 | for Version 1

Zakariyya Abdullahi Bature, King Mongkut's University of Technology Thonburi, Bangkok, Thailand

19 Views Cite this report Responses(2)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Machine Learning with interests in Computer Vision, Digital Image Processing, Feature Selection Methods, Optimization with their application to Eye Tracking, Human Gesture Recognition, Signal Processing, Biometrics Identification and related topics

Respond to this report

Responses (2)

Author Response

23 Oct 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

Thanks a lot Dr. Zakariyya Abdullahi
We will enhance the explanation of the algorithm, comparing it with prior works and highlighting how our system addresses the gaps. This will clearly detail how the eye gestures are translated into commands, emphasizing the novelty of our approach.
We will elaborate on the dataset used, specifying participant details, diversity, and dataset composition to ensure the robustness of the algorithm is clear.
We will include visual Confusion Matrix at the end of the results section, to better illustrate the system's performance and its effectiveness across various gestures.
We will clarify that the dataset used in this study was collected from 100 volunteers, ensuring a diverse representation in terms of age, gender, and ethnicity. This dataset, comprising 20,000 gesture instances, was instrumental in achieving a high accuracy rate of 99.63%. However, we acknowledge the importance of further validating the system using publicly available datasets to enhance generalizability. We are currently exploring the integration of external datasets, such as those from Dryad, to perform comparative analysis and further assess the robustness of our model in different scenarios.
We will explicitly state the potential limitations of the method, including the challenges of using the system in low-light conditions and potential issues with glasses or contact lenses.

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Competing Interests

No competing interests were disclosed.

Author Response

18 Dec 2024

Nachaat Mohamed, HLS, Rabdan Academy, Abu Dhabi, United Arab Emirates

Thank you for highlighting this point. We have revised the manuscript to provide a detailed description of the algorithm. The updated text includes an explanation of how the system uses machine learning models integrated with OpenCV and PyAutoGUI to capture eye movements and translate them into actionable commands. Additionally, we have added a comparison with prior works, emphasizing the improvements in speed, accuracy, and generalizability. These changes can be found in the revised Methodology and Related Work sections.

We appreciate this observation and have updated the manuscript to include comprehensive details about the dataset. Specifically, the dataset comprises 20,000 gesture instances collected from 100 volunteers, each representing a diverse range of demographics. Each participant performed 10 distinct gestures, repeated 20 times. These details, along with the methodology for dataset collection and preprocessing, are now clearly outlined in the Methodology section.

Thank you for this suggestion. We have incorporated visual representations, including a confusion matrix and bar charts, to present the system's performance across the 10 distinct gestures. These additions are now included in the Results section, providing a clearer understanding of classification accuracy, potential misclassifications, and overall performance metrics.

We acknowledge the importance of validating the system with publicly available datasets. While the primary dataset used in this study was collected from 100 volunteers to ensure diversity and robustness, we are actively exploring external datasets, such as those from Dryad, for further validation and comparison with other state-of-the-art methods. This effort will be part of our future work and has been discussed in the revised Results and Conclusion sections.

Thank you for this recommendation. We have updated the Conclusion section to explicitly discuss the system's limitations. These include minor accuracy reductions in scenarios involving reflective glasses (98.9%) and the need for further validation with publicly available datasets to enhance generalizability. We believe this addition provides a more balanced and complete assessment of the system.

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Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

[1] 1. Kirbis M, Kramberger I: Mobile device for electronic eye gesture recognition. IEEE Trans. Consum. Electron. November 2009; 55(4): 2127–2133. Publisher Full Text

[2] 2. Pingali TR, et al.: Eye-gesture controlled intelligent wheelchair using Electro-Oculography. 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, VIC, Australia. 2014; pp. 2065–2068. Publisher Full Text

[3] 3. Tanwear A, et al.: Spintronic Sensors Based on Magnetic Tunnel Junctions for Wireless Eye Movement Gesture Control. IEEE Trans. Biomed. Circuits Syst. Dec. 2020; 14(6): 1299–1310. PubMed Abstract | Publisher Full Text

[4] 4. Marina-Miranda J, Traver VJ: Head and Eye Egocentric Gesture Recognition for Human-Robot Interaction Using Eyewear Cameras. IEEE Robot. Autom. Lett. July 2022; 7(3): 7067–7074. Publisher Full Text

[5] 5. Lin M, Li B: A wireless EOG-based Human Computer Interface. 2010 3rd International Conference on Biomedical Engineering and Informatics, Yantai, China. 2010; pp. 1794–1796. Publisher Full Text

[6] 6. Morency L-P, Quattoni A, Darrell T: Latent-Dynamic Discriminative Models for Continuous Gesture Recognition. 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA. 2007; pp. 1–8. Publisher Full Text

[7] 7. Kulkarni G, Kulkarni R, Deshmukh V: Eye Gesture Interface and Emotion Detection Tool. 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India. 2023; pp. 1–6. Publisher Full Text

[8] 8. Liang Y, Samtani S, Guo B, et al.: Behavioral biometrics for continuous authentication in the internet-of-things era: An artificial intelligence perspective. IEEE Internet Things J. 2020; 7(9): 9128–9143. Publisher Full Text

[9] 9. Wei L, Lin Y, Wang J, et al.: Time-frequency convolutional neural network for automatic sleep stage classification based on single-channel EEG. 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), IEEE. 2017, November; pp. 88–95.

[10] 10. Zheng WL, Liu W, Lu Y, et al.: Emotionmeter: A multimodal framework for recognizing human emotions. IEEE Trans. Cybern. 2018; 49(3): 1110–1122. PubMed Abstract | Publisher Full Text

[11] 11. Hossain Z, Shuvo MMH, Sarker P: Hardware and software implementation of real time electrooculogram (EOG) acquisition system to control computer cursor with eyeball movement. 2017 4th international conference on advances in electrical engineering (ICAEE), IEEE. 2017, September; pp. 132–137.

[12] 12. Singh J, Aggarwal R, Tiwari S, et al.: Exam Proctoring Classification Using Eye Gaze Detection. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), IEEE. 2022, October; pp. 371–376.

[13] 13. Venugopal D, Amudha J, Jyotsna C: Developing an application using eye tracker. 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE. 2016, May; pp. 1518–1522.

[14] 14. Mohamed N, Awasthi A, Kulkarni N, et al.: Decision Tree Based Data Pruning with the Estimation of Oversampling Attributes for the Secure Communication in IOT. Int. J. Intell. Syst. Appl. Eng. 2022; 10(2s): 212–216.

[15] 15. Mohammadpour M, Hashemi SMR, Houshmand N: Classification of EEG-based emotion for BCI applications. 2017 Artificial Intelligence and Robotics (IRANOPEN), IEEE. 2017, April; pp. 127–131.

[16] 16. Lawson AP, Mayer RE: The power of voice to convey emotion in multimedia instructional messages. Int. J. Artif. Intell. Educ. 2022; 32(4): 971–990. Publisher Full Text

[17] 17. Thapaliya S, Jayarathna S, Jaime M: Evaluating the EEG and eye movements for autism spectrum disorder. 2018 IEEE International Conference on Big Data (Big Data), IEEE. 2018, December; pp. 2328–2336.

[18] 18. Mohamed N: Study of bypassing Microsoft Windows Security using the MITRE CALDERA Framework. F1000Res. 2022; 11: 422. PubMed Abstract | Publisher Full Text | Free Full Text

[19] 19. Wang KJ, Zheng CY, Mao ZH: Human-centered, ergonomic wearable device with computer vision augmented intelligence for VR multimodal human-smart home object interaction. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), IEEE. 2019, March; pp. 767–768.

[20] 20. Liang X, Ghannam R, Heidari H: Wrist-worn gesture sensing with wearable intelligence. IEEE Sensors J. 2018; 19(3).

[21] 21. Holmes M, Latham A, Crockett K, et al.: Near real-time comprehension classification with artificial neural networks: Decoding e-learner non-verbal behavior. IEEE Trans. Learn. Technol. 2017; 11(1): 5–12. Publisher Full Text

[22] 22. Fridman L, Langhans P, Lee J, et al.: Driver gaze region estimation without use of eye movement. IEEE Intell. Syst. 2016; 31(3): 49–56. Publisher Full Text

[23] 23. Alam MM, Raihan MMS, Chowdhury MR, et al.: High Precision Eye Tracking Based on Electrooculography (EOG) Signal Using Artificial Neural Network (ANN) for Smart Technology Application. 2021 24th International Conference on Computer and Information Technology (ICCIT), IEEE. 2021, December; pp. 1–6.

[24] 24. Lee TM, Yoon JC, Lee IK: Motion sickness prediction in stereoscopic videos using 3d convolutional neural networks. IEEE Trans. Vis. Comput. Graph. 2019; 25(5): 1919–1927. PubMed Abstract | Publisher Full Text

[25] 25. Zhang J, Tao D: Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet Things J. 2020; 8(10): 7789–7817. Publisher Full Text

[26] 26. Abiodun OI, Jantan A, Omolara AE, et al.: State-of-the-art in artificial neural network applications: A survey. Heliyon. 2018; 4(11): e00938. PubMed Abstract | Publisher Full Text | Free Full Text

[27] 27. Khosravi N, Abdolvand A, Oubelaid A, et al.: Improvement of power quality parameters using modulated-unified power quality conditioner and switched-inductor boost converter by the optimization techniques for a hybrid AC/DC microgrid. Sci. Rep. 2022; 12(1): 1–20. Publisher Full Text

[28] 28. Mohamed NA, Jantan A, Abiodun OI: An improved behaviour specification to stop advanced persistent threat on governments and organizations network. Proceedings of the International MultiConference of Engineers and Computer Scientists. 2018; Vol. 1: pp. 14–16.

[29] 29. Mohamed N, Alam E, Stubbs GL: Multi-layer protection approach MLPA for the detection of advanced persistent threat. J. Posit. School Psychol. 2022; 4496–4518.

[30] 30. Omolara AE, Jantan A, Abiodun OI, et al.: Fingereye: improvising security and optimizing ATM transaction time based on iris-scan authentication. Int. J. Electr. Comput. Eng. 2019; 9(3): 1879. Publisher Full Text

[31] 31. Mohamed N: State-of-the-Art in Chinese APT Attack and Using Threat Intelligence for Detection. A Survey. J. Posit. School Psychol. 2022; 4419–4443.

[32] 32. Mohamed N, Almazrouei SK, Oubelaid A, et al.: Air-Gapped Networks: Exfiltration without Privilege Escalation for Military and Police Units. Wirel. Commun. Mob. Comput. 2022; 2022: 1–11. Publisher Full Text

[33] 33. Oubelaid A, Taib N, Nikolovski S, et al.: Intelligent speed control and performance investigation of a vector controlled electric vehicle considering driving cycles. Electronics. 2022; 11(13): 1925. Publisher Full Text

[34] 34. Mohamed N, Kumar KS, Sharma S, et al.: Wireless Sensor Network Security with the Probability Based Neighbourhood Estimation. Int. J. Intell. Syst. Appl. Eng. 2022; 10(2s): 231–235.

[35] 35. Mohamed NA, Jantan A, Abiodun OI: Protect Governments, and organizations Infrastructure against Cyber Terrorism (Mitigation and Stop of Server Message Block (SMB) Remote Code Execution Attack). Int. J. Eng. 2018; 11(2): 261–272.

[36] 36. Yao L, Park M, Grag S, et al.: Eye Movement and Visual Target Synchronization Level Detection Using Deep Learning. Australasian Joint Conference on Artificial Intelligence. Cham: Springer; 2022, February; pp. 668–678.

[37] 37. Yaneva V, Eraslan S, Yesilada Y, et al.: Detecting high-functioning autism in adults using eye tracking and machine learning. IEEE Trans. Neural Syst. Rehabil. Eng. 2020; 28(6): 1254–1261. PubMed Abstract | Publisher Full Text

[38] 38. Kumar BV, Srinivas KK, Anudeep P, et al.: Artificial Intelligence Based Algorithms for Driver Distraction Detection: A Review. 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), IEEE. 2021, October; pp. 383–386.

[39] 39. Lin W, Kotakehara Y, Hirota Y, et al.: Modeling reading behaviors: An automatic approach to eye movement analytics. IEEE Access. 2021; 9: 63580–63590. Publisher Full Text

[40] 40. Jiang M, Francis SM, Srishyla D, et al.: Classifying individuals with ASD through facial emotion recognition and eye-tracking. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE. 2019, July; pp. 6063–6068.

[41] 41. Putra RY, Kautsar S, Adhitya RY, et al.: Neural network implementation for invers kinematic model of arm drawing robot. 2016 International Symposium on Electronics and Smart Devices (ISESD), IEEE. 2016, November; pp. 153–157.

[42] 42. Ramkumar S, Kumar KS, Emayavaramban G: A feasibility study on eye movements using electrooculogram based HCI. 2017 International Conference on Intelligent Sustainable Systems (ICISS), IEEE. 2017, December; pp. 380–383.

[43] 43. Li L, Godaba H, Ren H, et al.: Bioinspired soft actuators for eyeball motions in humanoid robots. IEEE/ASME Transactions on Mechatronics. 2018; 24(1): 100–108. Publisher Full Text

[44] 44. Castellanos JL, Gomez MF, Adams KD: Using machine learning based on eye gaze to predict targets: An exploratory study. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE. 2017, November; pp. 1–7.

[45] 45. Wang W, Lee J, Harrou F, et al.: Early detection of Parkinson’s disease using deep learning and machine lea.2020.

[46] 46. Akshay S, Megha YJ, Shetty CB: Machine learning algorithm to identify eye movement metrics using raw eye tracking data. 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE. 2020, August; pp. 949–955.

[47] 47. Ramzan M, Khan HU, Awan SM, et al.: A survey on state-of-the-art drowsiness detection techniques. IEEE Access. 2019; 7: 61904–61919. Publisher Full Text

[48] 48. Kruthiventi SS, Ayush K, Babu RV: Deepfix: A fully convolutional neural network for predicting human eye fixations. IEEE Trans. Image Process. 2017; 26(9): 4446–4456. PubMed Abstract | Publisher Full Text

[49] 49. Challa KNR, Pagolu VS, Panda G, et al.: An improved approach for prediction of Parkinson’s disease using machine learning techniques. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), IEEE. 2016, October; pp. 1446–1451.

[50] 50. Mohammadpour M, Khaliliardali H, Hashemi SMR, et al.: Facial emotion recognition using deep convolutional networks. 2017 IEEE 4th international conference on knowledge-based engineering and innovation (KBEI), IEEE. 2017, December; pp. 0017–0021.

[51] 51. Lemley J, Kar A, Drimbarean A, et al.: Convolutional neural network implementation for eye-gaze estimation on low-quality consumer imaging systems. IEEE Trans. Consum. Electron. 2019; 65(2): 179–187. Publisher Full Text

[52] 52. Mohamed N, Belaton B: SBI model for the detection of advanced persistent threat based on strange behavior of using credential dumping technique. IEEE Access. 2021; 9: 42919–42932. Publisher Full Text

[53] 53. Mohamed NA, Jantan A, Omolara AE: Mitigation of Cyber Terrorism at ATMs, and Using DNA, Fingerprint, Mobile Banking App to withdraw cash (Connected with IoT).

[54] 54. Mohamed NAE, Jantan A, Omolara AE: Mitigation of Cyber Terrorism at ATMs, and Using DNA, Fingerprint, Mobile Banking App to withdraw cash (Connected with IoT).

[55] 55. Jie L, Jian C, Lei W: Design of multi-mode UAV human-computer interaction system. 2017 IEEE international conference on unmanned systems (ICUS), IEEE. 2017, October; pp. 353–357.

[56] 56. Oravec JA: The emergence of “truth machines”?: Artificial intelligence approaches to lie detection. Ethics Inf. Technol. 2022; 24(1): 1–10. Publisher Full Text

[57] 57. Mengi M, Malhotra D: Artificial intelligence based techniques for the detection of socio-behavioral disorders: a systematic review. Arch. Comput. Methods Eng. 2022; 29(5): 2811–2855. Publisher Full Text

[58] 58. Yao L, Park M, Grag S, et al.: Eye Movement and Visual Target Synchronization Level Detection Using Deep Learning. Australasian Joint Conference on Artificial Intelligence. Cham: Springer; 2022, February; pp. 668–678.

[59] 59. Wang KJ, Liu Q, Zhao Y, et al.: Intelligent wearable virtual reality (VR) gaming controller for people with motor disabilities. 2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), IEEE. 2018, December; pp. 161–164.

[60] 60. Chen H, Zendehdel N, Leu MC, et al.: Real-time human-computer interaction using eye gazes. Manuf. Lett. 2023; 35: 883–894. Publisher Full Text

[61] 61. Chen H, Leu MC, Yin Z: Real-time multi-modal human–robot collaboration using gestures and speech. J. Manuf. Sci. Eng. 2022; 144(10):101007. Publisher Full Text

[62] 62. Chen H, Zendehdel N, Leu MC, et al.: Fine-grained activity classification in assembly based on multi-visual modalities. J. Intell. Manuf. 2024; 35(5): 2215–2233. Publisher Full Text

[63] 63. Meena G, Mohbey KK, Lokesh K: FSTL-SA: few-shot transfer learning for sentiment analysis from facial expressions. Multimed. Tools Appl. 2024: 1–29. Publisher Full Text

[64] 64. Meena G, Mohbey KK, Kumar S: Monkeypox recognition and prediction from visuals using deep transfer learning-based neural networks. Multimed. Tools Appl. 2024; 83: 71695–71719. Publisher Full Text

[65] 65. Belaid S, Rekioua D, Oubelaid A, et al.: A power management control and optimization of a wind turbine with battery storage system. J. Energy Storage. 2022; 45: 103613. Publisher Full Text

[66] 66. Rong Y, Han C, Hellert C, et al.: Artificial intelligence methods in in-cabin use cases: a survey. IEEE Intell. Transp. Syst. Mag. 2021; 14(3): 132–145. Publisher Full Text

[67] 67. Dr. Mohamed N : Nachaat3040/Eye-Gesture-: Eye-Gesture- 1.0 (AI). [Dataset]. Zenodo. 2023. Publisher Full Text

Eye-gesture control of computer systems via artificial intelligence

Abstract

Background

Methods

Results

Conclusions

Keywords

Revised Amendments from Version 2

Introduction

Figure 1. Comparison of Ease of Use between Traditional Input Devices and Eye-Tracking Technologies for Different User Group.

Problem statement

Background

Motivations

Related work

Table 1. Comparative Analysis of Eye-Gesture Recognition and Related Systems.

Methods

Figure 2. AI-based eye mouse gestures steps.

Model parameter comparison

Dataset collection and composition

Algorithm and system development

Model training and testing

Model fine-tuning process

Advancements and real-world applicability

Results and discussion

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Figure 3. Recognizing the user's face in order to identify the eyes.

Figure 4. Identify the eyes and determine which eye will wink to squeeze the mouse and which eye the mouse will fixate.

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Figure 5. Plot of AI-based eye mouse gestures (accutecy).

Figure 6. Plot of AI-based eye mouse gestures (accutecy).

Figure 7. Plot of AI-based eye mouse gestures (accutecy).

Figure 8. Plot of AI-based eye mouse gestures (accutecy).

Table 2. Linear regression of AI-based eye mouse gestures.

Table 3. Compare The AI Eye gesture control with the rest in literature.

Table 4. Confusion matrix for eye gesture recognition.

User comfort and long-term usability

Challenges and limitations

Privacy and data protection

Conclusion

Future work

Ethical and informed consent for data usage

Data availability

Acknowledgment

References

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