Smart AI Based Eye Controlled Wheelchair

References

[1] A. Rajesh and M. Mantur, "Eyeball gesture controlled automatic wheelchair using deep learning," presented at the 5th IEEE Region 10 Humanitarian Technology Conference (R10-HTC 2017), 2018, pp. 387-391.

[2] M. Dahmani, M. E. H. Chowdhury, A. Khandakar, T. Rahman, K. Al-Jayyousi, A. Hefny, and S. Kiranyaz, "An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control," Sensors, vol. 20, no. 14, 2020.

[3] S. Higa, K. Yamada, and S. Kamisato, "Intelligent Eye-Controlled Electric Wheelchair Based on Estimating Visual Intentions Using One -Dimensional Convolutional Neural Network and Long Short-Term Memory," Sensors, vol. 23, no. 8, 2023.

[4] M. Subramanian, N. Songur, D. Adjei, P. Orlov, and A. A. Faisal, "A.Eye Drive: Gaze- based semi-autonomous wheelchair interface," presented at the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 5967-5970.

[5] V. D. Tran and C. T. Nguyen, "A Computer Vision-Based Eye-Tracking System Toward an Eye-Controlled Powered Wheelchair," in Computational Intelligence Methods for Green Technology and Sustainable Development, Lecture Notes in Networks and Systems, Springer Nature, 2024, pp. 290-300.

[6] J. Xu et al., "Eye-Gaze Controlled Wheelchair Based on Deep Learning," vol. 23, no. 13, 2023.

[7] P. Azizinezhad, "Development of adaptive gaze-based human-machine interface for enhancing assisted daily living," Doctoral thesis, University of Essex, 2025.

[8] W. Fuhl, G. Kasneci, and E. Kasneci, "TEyeD: Over 20 Million Real-World Eye Images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types," presented at the 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2021.

[9] R. Fusek and E. Sojka, "Iris Center Localization Using Geodesic Distance and CNN," in Pattern Recognition and Image Analysis: 9th Iberian Conference (IbPRIA 2019), Madrid, Spain, July 2019, Proceedings, Part II, Springer-Verlag, 2019, pp. 76-85.

[10] D. Cojocaru et al., "Using an eye gaze new combined approach to control a wheelchair movement," presented at the 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC), 2019.

[11] N. Wanluk et al., "Smart wheelchair based on eye tracking," presented at the 2016 9th Biomedical Engineering International Conference (BMEiCON), 2016, pp. 1-4.

[12] W. Luo et al., "A Human-Computer Control System Based on Intelligent Recognition of Eye Movements and Its Application in Wheelchair Driving," vol. 5, no. 9, 2021.

[13] E. Wästlund et al., "Evaluating gaze-driven power wheelchair with navigation support for persons with disabilities," Journal of Rehabilitation Research and Development, vol. 52, pp. 815-826, 2015.

[14] D. Bai et al., "Design of an eye movement- controlled wheelchair using Kalman filter algorithm," presented at the 2016 IEEE International Conference on Information and Automation (ICIA), 2016.

[15] F. P. Chee et al., "Eye tracking electronic wheelchair for physically challenged person," ARPN Journal of Engineering and Applied Sciences, vol. 12, pp. 4078-4083, 2017.

@article{1717823,
      author = {Karthikeyan P, Siva S, Pandikannan A, Dr. M. Iiayaraja},
      title = {Smart AI Based Eye Controlled Wheelchair},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {1895-1902},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717823.pdf},
      abstract = {This paper presents an eye-controlled wheelchair system designed to restore independent mobility for individuals with severe physical disabilities such as quadriplegia, who are unable to operate traditional joystick-controlled wheelchairs. The proposed system integrates a camera module for real-time eye image capture, followed by image preprocessing, eye detection, and eye tracking modules to isolate and monitor gaze direction. A Convolutional Neural Network (CNN) classifies the user's gaze into three discrete commands—left, right, and center—which are interpreted by the command module to control wheelchair movement accordingly. These commands are transmitted via an Arduino communication module to a motor driver that actuates high-torque DC motors, enabling forward, backward, and stop functions. The system incorporates robust safety mechanisms, including an automatic stop when eyes are centered or closed, ensuring reliable operation in real-world environments. Experimental results demonstrate that the CNN-based classification achieves high accuracy under varying lighting conditions and with minor head movements, providing an intuitive, hands-free, and low-cost assistive solution that significantly enhances user independence and quality of life.},
      keywords = {Eye-Controlled Wheelchair, Convolutional Neural Network (CNN), Gaze Classification, Assistive Technology, Real-Time Image Processing, Hands-Free Navigation},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717823}
  }