Selecting Control Menu on Electric Wheelchair Using Eyeball Movement for Difable Person

Fitri Utaminingrum - Brawijaya University
I Komang Somawirata - National Institute of Technology (ITN Malang)
Gusti Pengestu - Bina Nusantara University
Tipajin Thaipisutikul - Mahidol University
Timothy Shih - National Central University

Citation Format:



The number of people with disabilities and strokes in each country always increases yearly. Hand defects and stroke make them have limitations in doing activities. It caused their hand has paralyzed. Hence, they find it difficult to do daily activities, such as running a wheelchair, choosing a menu on the screen display, and so on. One solution offered is utilizing eye movement as a navigation tool that can replace the role of the user's hand, so they can run a wheelchair independently or choose a menu selection on display by themselves through the movement of their eyes. Detection of eyeball movements in this study only utilizes a camera mounted in front of the user so that it is more practical and easier to use than if we have to pair an electrooculography sensor in the area around the user's eyes. This research pro-posed a new approach to detect the five gazes (upward, downward, leftward, rightward, and forward) of the eyeball movements by using Backpropagation Neural Network (BPNN) and Dynamic Line Sector Coordinate (DLSC). Our proposed method can detect five gaze directions and select four menus on the display monitor. The mean accuracy of our proposed method to detect eye movements for each gaze is 88.6%.


Disabilities, Detection, Eyeball Movements, Eyeball Movements, Dynamic Line Sector Coordinate


B. Vijayalaxmi, C. Anuradha, K. Sekaran, M.N. Meqdad & S. Kadry, Image processing based eye detection methods a theoretical review, Bulletin of Electrical Engineering and Informatics, 9 (3), 1189-1197, 2020.

S. S. Shylaja, K.N.B. Murthy, S. N. Nischith, R. Muthuraj & S. Ajay, Feed forward neural network based eye localization and recognition using Hough transform, International journal of advanced computer science and applications, 2 (3), 104-109, 2011. DOI: 10.14569/IJACSA.2011.020318

Vijayalaxmi, & Sreehari, Knowledge based template for Eye detection, National Conference on Microwave, Antenna and Signal Processing, 90-94, 2011.

F. Wadehn, T. Weber, D. J. Mack, T. Heldt, & H. -A. Loeliger, Model-Based Separation, Detection, and Classification of Eye Movements," in IEEE Transactions on Biomedical Engineering, 67, 588-600, 2020. doi: 10.1109/TBME.2019.2918986

B. Yan, T. Pei, & X. Wang, Wavelet Method for Automatic Detection of Eye-Movement Behaviors, in IEEE Sensors Journal, 19, 3085-3091, 2019. doi: 10.1109/JSEN.2018.2876940

F. Sadikoglu, & S. Uzelaltinbulat, Biometric Retina Identification Based on Neural Network, Procedia Computer Science, 102, 26–33, 2016.

J. Rose, Y. Liu, & A. Awad, Biometric Authentication Using Mouse and Eye Movement Data, IEEE Security and Privacy Workshops (SPW), 47–55, 2017. DOI: 10.1109/SPW.2017.18

S. Plesnick, D. Repice, & P. Loughnane, Eye-controlled wheelchair, IEEE Canada International Humanitarian Technology Conference, 1-4, 2014. DOI: 10.1109/IHTC.2014.7147553

F. Utaminingrum, P. P. Adikara, Y. A. Sari, D. Syauqy, & A. G. Hapsani, Left-Right Head Movement for Controlling Smart Wheelchair By Using Centroid Coordinates Distance, Journal of Theoretical and Applied Information Technology, 96 (19), 2852-2861, 2018.

B. W. Miller, Using Reading Times and Eye-Movements to Measure Cognitive Engagement, Educational Psychologist, 50 (1), 31–42, 2015.

A. Sitipitakchai & S. Phimoltares, Eye-Captcha: An Enhanced Captcha Using Eye Movement, IEEE International Conference on Computer and Communications (ICCC). 2120-2126, 2017. DOI: 10.1109/CompComm.2017.8322911

C. Atmaji, A. E. Putra & A. Hanif, Sliding window method for eye movement detection based on electrooculogram signal, International Conference on Information and Communications Technology, ICOIACT, 628–632, 2018. DOI: 10.1109/ICOIACT.2018.8350779

A. Bulling, J. A. Ward, H. Gellersen, & G. Troster, Eye Movement Analysis for Activity Recognition Using Electrooculography, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (4), 741–753, 2011. DOI: 10.1109/TPAMI.2010.86

K. Arai, Mobile Phone Operations Using Human Eye Only and Its Applications, International Journal of Advanced Computer Science and Applications, 9, 148–154, 2018. DOI: 10.14569/IJACSA.2018.090322

F. Utaminingrum, M. A. Fauzi, & Y. A. Sari, Eye Movement as Navigator for Restricted Disabled Person in Handling Position, ICCIS International Conference on Communication and Information System, 1–5, 2017.

S. N. Patel, & V. Prakash, Autonomous camera based eye controlled wheelchair system using raspberry-pi, IEEE International Conference on Innovations in Information, Embedded and Communication Systems, 3–8, 2015. 10.1109/ICIIECS.2015.7192876

R. P. Prasetya & F. Utaminingrum, Triangle similarity approach for detecting eyeball movement, 5th International Symposium on Computational and Business Intelligence, ISCBI, 37–40, 2017. DOI: 10.1109/ISCBI.2017.8053540

G. Pangestu, F. Utaminingrum & F. A. Bachtiar, Eyeball Movement Detection System using Corner Triangle Similarity, Na ̈ıve Bayes, and Ear Approach, International Journal of Advances in Soft Computing and its Applications, 11, 1–14, 2019.

Yaiprasert & Chairote, Artificial Intelligence for Para Rubber Identification Combining Five Machine Learning Methods, Karbala International Journal of Modern Science: 7, 257-267, 2021.

I. M. Saiful, A. U. Muhammad & P. D. Jitu, An imperceptible & robust digital image watermarking scheme based on DWT, entropy and neural network, Karbala International Journal of Modern Science, 5, 36-44, 2019.

P. Viola, & M. J. Jones, Robust real-time face detection, International Journal of Computer Vision, 57 (2), 137–154, 2004.

F. N. Ibrahim, Z. M. Zin & N. Ibrahim, Eye Center Detection Using Combined Viola-Jones and Neural Network Algorithms, International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR), 1-6, 2018. DOI: 10.1109/ISAMSR.2018.8540543

A. Priadana, & M. Habibi, Face Detection using Haar Cascades to Filter Selfie Face Image on Instagram, International Conference of Artificial Intelligence and Information Technology (ICAIIT), 6-9, 2019. DOI: 10.1109/ICAIIT.2019.8834526

M. S. Uddin & A. Y. Akhi, Horse Detection Using Haar like Features, International Journal of Computer Theory and Engineering, 8 (5), 415–418, DOI: 10.7763/IJCTE.2016.V8.1081

W. R. Gowers, The Movements of the Eyelids, Journal of the Royal Society of Medicine, 62 (1), 429–440, 2015.

H. Wang, Linear Algebra Online Interactive Guiding Innovation Based on Big Data and Eye Movement Monitoring, 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 924-927, 2021. doi: 10.1109/ICICCS51141.2021.9432351

S. Habib, I. Khan, S. Aladhadh, M. Islam & S. Khan, External Features-Based Approach to Date Grading and Analysis with Image Processing, Emerging Science Journal, 6 (4), 694-704, 2022.

F. Laurene, Fundamentals of Neural Network, Architectures, Algorithm And Applications, Prentice Hall, Upper Sadle River, New Jersey, 1994.

G. Pangestu, F. Utaminingrum & F. A. Bachtiar, Eye State Recognition Using Multiple Methods for Applied to Control Smart Wheelchair, The Intelligent Networks and Systems Society, 12 (1), 232–241, 2019. DOI: 10.22266/ijies2019.0228.23

M. Singh, P. Jain, & Chopra, Eye movement detection for wheelchair control application, International Conference on Electrical, Electronics, Signals, Communication and Optimization, EESCO, 4–8, 2015. DOI:10.1109/EESCO.2015.7253877

Z. A. Haq, & Z. Hasan, Eye-blink rate detection for fatigue determination, India International Conference on Information Processing, IICIP, 1-5, 2016. DOI: 10.1109/IICIP.2016.7975348


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JOIV : International Journal on Informatics Visualization
ISSN 2549-9610  (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W :
E :,,

View JOIV Stats

Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.