Assistive Eye Blink Technology for Paralyzed Patient

Abstract

Assistive Eye Blink Technology helps to communicate with the individuals having paralysis by analyzing their eye patterns and detect eye blink using OpenCV and computer vision. It performs tasks using different blink patterns and has emergency alert. It does not require caressing and is cost-effective solution which creates a sense of independence in the users and also helps them to perform their day-to-day tasks.

Introduction

Purpose

This system enables people with severe physical disabilities (e.g., ALS, spinal injuries) to communicate using eye blinks and eye movements, replacing traditional verbal or physical communication methods. It leverages facial landmark detection and real-time video processing to track eye blinks, enabling the user to send messages or interact with devices.

Key Objectives

• Help paralyzed patients communicate and perform tasks independently.

• Provide a customizable blink pattern interface.

• Offer real-time, accurate, and cost-effective communication.

• Use low-resource and easily accessible technologies.

Literature Review Highlights

1. Eye-LRCN: Combines CNN and RNN for blink pattern analysis, aiding in diagnosing Computer Vision Syndrome.

2. Blink-based Typing: Uses DLib and SVM for on-screen keyboard navigation via blinking; employs transfer learning.

3. RT-BENE Dataset: Offers real-time blink detection using CNNs for public and HCI settings.

4. EAR Method: Detects eye blinks using Eye Aspect Ratio (EAR) from facial landmarks; suitable for fatigue detection.

5. Lightweight CNN Models: Optimized for low-power devices like wearables, supporting mobile healthcare.

Existing Systems

• Tobii/EyeTech: Infrared-based commercial eye-trackers.

• EMG Sensors: Detect muscle movements near the eyes.

• BCI Systems: Advanced but expensive and complex neural signal processors.

• Gaze-based Systems: Allow selection by staring at the screen but are often less efficient than blink-based methods.

System Implementation

• Uses a webcam and OpenCV to detect and process facial features in real time.

• CNN (Convolutional Neural Network) identifies key facial landmarks (eyes, eyebrows, lips).

• On blink detection, the system sends voice messages to caregivers for communication.

• Built using Python and developed in Visual Studio.

System Workflow

1. System starts and detects the user's face.

2. Tracks eye shape and movement.

3. Detects a blink and verifies its validity.

4. Executes a predefined action if the blink is valid.

5. Ignores invalid or accidental blinks.

Results

• The system successfully detects valid eye blinks in real time.

• Enables basic communication for disabled users with minimal setup.

Future Scope

• Use blink input to control smart devices, wheelchairs, or home appliances.

• Integrate with music apps for play/pause/skip control.

• Select and listen to audiobooks through blink navigation.

• Improve accuracy using AI to distinguish between intentional and accidental blinks.

Limitations

• Requires natural lighting; low-light conditions reduce accuracy.

• Background movement can interfere with blink detection.

• User fatigue may occur with extended usage.

• Single input method—temporary eye issues can limit usability.

Conclusion

Assistive eye blink technology offers a solution for people having paralysis. This system allow them to connect and interact more effectively by using eye blink. It provides them new and better way to communicate. By using eye-tracking techniques, users can associate themselves more effectively though eye movements. The user-friendly web interface provides easy accessibility for users. It is more than technology; it gives people a voice and helps them to be independent and feel confident and connected.

References

[1] G. de la Cruz, M. Lira, O. Luaces, and B. Remeseiro, “Eye-LRCN: A Long-Term Recurrent Convolutional Network for Eye Blink Completeness Detection,” Neural Networks, 2024. [2] P. B. Jain, S. Bhat, G. Pujari, V. Hiremath, and D. C., “Eye Typing - Vision Based Human Activity Control,” in Proceedings of IEEE International Conference on Signal Processing, Communication, and Engineering Systems, 2022. [3] P. A. Shinde, S. N. Lanjwal, R. A. Kalantre, and S. P. Pawar, “Eye Blink Based Typing Software for Paralyzed Patient,” in Proceedings of International Conference on Recent Trends in Engineering and Technology, 2021. [4] K. Cortacero, T. Fischer, and Y. Demiris, “RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [5] 5. T. Appel, T. Santini, and E. Kasneci, “Brightness- and Motion-Based Blink Detection for Head-Mounted Eye Trackers,” in Proceedings of ACM Symposium on Eye Tracking Research and Applications (ETRA), 2016.. [6] T. Soukupová and J. Cech, “Real-Time Eye Blink Detection Using Facial Landmarks,” in Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP), 2016.. [7] P. Fogelton and W. Benesova, “Eye Blink Completeness Detection,” in Proceedings of International Conference on Image Processing Theory, Tools and Applications (IPTA), 2018. [8] M. Jordan, A. Pegatoquet, A. Castagnetti, J. Raybaut, and P. L. Coz, “Deep Learning for Eye Blink Detection Implemented at the Edge,” in Proceedings of IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2020.

Copyright

Copyright © 2025 Dr. J. W. Bakal, Abhijeet Dhekane, Vedika Patne, Aishwarya Chavan, Sanika Sanap. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.