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International Journal of Science, Strategic Management and Technology

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ISSN: 3108-1762 (Online)
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SMART WHEELCHAIR KIT FOR PARALYZED PATIENTS WITH EFFECTIVE EMG AND EOG CONTROLS

AUTHORS:
R.S.Janani
S.Bharat
P.Gopurajeyam
M.Karthikeyan
Mentor
Affiliation
Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy, India
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

This project presents the development of an intelligent assistive mobility system designed to enhance the independence of individuals with severe paralysis through a retrofittable smart wheelchair kit. The system integrates multiple control mechanisms, including Electromyography (EMG), Electrooculography (EOG), joystick-based manual control, and Bluetooth-enabled wireless operation, enabling flexible and user-adaptive navigation. EMG signals obtained from voluntary muscle activity and EOG signals derived from eye movements are continuously acquired and processed to detect user intent. These bio-signals undergo signal conditioning, filtering, and threshold-based decision-making to generate accurate directional commands for wheelchair movement, ensuring reliable and responsive control even for users with minimal physical capability. A microcontroller-based control unit manages all inputs and interfaces with motor drivers to regulate speed and direction. Performance evaluation is conducted based on response time, control accuracy, and user adaptability, demonstrating that the multi-modal approach significantly improves accessibility and usability while maintaining low implementation cost and practical feasibility.

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R.S.Janani, , S.Bharat, , P.Gopurajeyam, & M.Karthikeyan, (2026). Smart Wheelchair Kit for Paralyzed Patients with Effective EMG and EOG Controls. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.231

R.S.Janani, , et al.. "Smart Wheelchair Kit for Paralyzed Patients with Effective EMG and EOG Controls." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.231.

R.S.Janani, , S.Bharat, P.Gopurajeyam, and M.Karthikeyan. "Smart Wheelchair Kit for Paralyzed Patients with Effective EMG and EOG Controls." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.231.

References
1.Barea, L. Boquete, M. Mazo and E. López, "System for Assisted Mobility Using Eye Movements Based on Electrooculography," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 10, no. 4, pp. 209-218, Dec. 2002, doi: 10.1109/TNSRE.2002.806832.

2.D. Kumar and P. K. Biswas, "Design of EMG Controlled Wheelchair for Physically Disabled Persons," International Journal of Engineering Research & Technology (IJERT), vol. 4, no. 3, pp. 1-5, 2015.

3.K. Gupta and S. K. Arora, "Arduino Based Smart Wheelchair Control System Using Bluetooth," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 6, no. 5, pp. 3200-3205, 2017.

4.Tavakoli, L. Carriere and A. Torabi, "Robotic Wheelchair Control Using Brain-Computer Interface: A Review," Journal of Rehabilitation Research and Development, vol. 51, no. 2, pp. 155-170, 2014, doi: 10.1682/JRRD.2013.02.0046.

5.Phinyomark, P. Phukpattaranont and C. Limsakul, "Feature Reduction and Selection for EMG Signal Classification," Expert Systems with Applications, vol. 39, no. 8, pp. 7420-7431, 2012, doi: 10.1016/j.eswa.2012.01.102.

6.Arduino, "Arduino Uno Rev3 Datasheet," [Online]. Available: https://www.arduino.cc(Accessed: Mar. 2026).

6.STMicroelectronics, "L298N Dual Full-Bridge Driver Datasheet," [Online]. Available: https://www.st.com(Accessed: Mar. 2026).

7.Upside Down Labs, "BioAmp EXG Pill – EMG, ECG, and EOG Sensor Documentation," [Online].Available: https://upsidedownlabs.tech(Accessed: Mar. 2026).

8.A. A. Al-Haddad, "Eye Movement Controlled Wheelchair Using EOG Signals," International Journal of Biomedical Engineering and Technology, vol. 21, no. 2, pp. 115-128, 2016.

9.Simpson, "Smart Wheelchairs: A Literature Review," Journal of Rehabilitation Research and Development, vol. 42, no. 4, pp. 423-436, 2005.
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✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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