Design and Implementation of a Low-Cost Wireless Hand-Gesture-Controlled Wheelchair Using MPU6050 and NRF24L01

Authors

  • Hisham Aboulghasem Ali Esherwi Lecturer at College of Computer Technology-Tripoli, Libya Author

DOI:

https://doi.org/10.65405/f6cghf48

Keywords:

MPU6050, NRF24L01, Arduino Nano, L298N Motor Driver, Assistive Mobility, Wireless Wheelchair Control

Abstract

This paper presents the design and implementation of a low-cost wireless hand-gesture-controlled wheelchair aimed at improving mobility and independence for individuals with physical disabilities. The proposed system uses an MPU6050 inertial measurement unit (IMU) embedded in a wearable glove to detect hand orientation and motion. The sensed data are processed by an Arduino Nano microcontroller and then transmitted wirelessly via an NRF24L01 module to a receiver unit installed on the wheelchair.

At the wheelchair side, a second Arduino Nano receives and interprets the transmitted commands, while an L298N motor driver controls two DC motors to perform directional movements, including forward, backward, left, right, and stop. The system architecture is designed with a strong focus on affordability, ease of use, and dependable performance.

Experimental testing under indoor conditions showed smooth system operation, stable wireless communication, accurate gesture recognition, and fast motor response. The findings indicate that IMU-based hand-gesture control can provide a practical and accessible alternative to conventional joystick-based wheelchair control, especially for users with limited upper-limb mobility.

Downloads

Download data is not yet available.

References

Branford, K. P.,Van Straten, M.G., Jahanian, O., Morrow, M. M. B. & Cain S. M. (2025). An inertial sensor-based comprehensive analysis of manual wheelchair user mobility during daily life in people with SCI. PLOS ONE, 20(8), e0323050. https://doi.org/10.1371/journal.pone.0323050

Haddoun, A., Djabri, D., Saidani, M. & Benbouzid, M. (2025). Development and evaluation of a head-controlled wheelchair using wearable inertial sensors. Engineering Science and Technology, an International Journal, 36, Article in press. https://doi.org/10.1016/j.mex.2025.103485

Han, Y., Zhou, L., Jiang,W. & Wang G. (2024). Intelligent wheelchair human–robot interactive system: A review. Journal of Mechanical Science and Technology, 38. https://doi.org/10.1007/s12206-024-0731-7

Iqbal, H., Zheng, J., Chai, R. & Chandrasekaran, S. (2024). Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals. Medical & Biological Engineering & Computing, 62, 167–182. https://doi.org/10.1007/s11517-023-02921-z

Kundu, A. S., Mazumder, O., Lenka, P. K., & Bhaumik, S. (2018). Hand gesture recognition-based omnidirectional wheelchair control using IMU and EMG sensors. Journal of Intelligent & Robotic Systems, 91, 529–541. https://doi.org/10.1007/s10846-017-0725-0

Miftahussalam, I., Julian, E. S., Prawiroredjo, K. & Djuana, E. (2023). Wheelchair control system with hand movement using accelerometer sensor. Microelectronic Engineering, 271, Article 112018. https://doi.org/10.1016/j.mee.2023.112018

Shahzad, W., Ayaz, Y., Khan, M. J., Naseer, N., & Khan, M. (2019). Enhanced performance for multi-forearm movement decoding using hybrid IMU/sEMG interface. Frontiers in Neurorobotics, 13, Article 43. https://doi.org/10.3389/fnbot.2019.00043

Zhang, D., Liao, Z., Xie, W., Wu, X., Xie, H. & Xiao, J. (2022). Fine-grained and real-time gesture recognition by using IMU data for assistive device control. IEEE Transactions on Mobile Computing, 21(12), 4421–4433. https://doi.org/10.1109/TMC.2021.3120475

Downloads

Published

2026-03-01

How to Cite

Design and Implementation of a Low-Cost Wireless Hand-Gesture-Controlled Wheelchair Using MPU6050 and NRF24L01. (2026). Comprehensive Journal of Science, 10(39), 390-400. https://doi.org/10.65405/f6cghf48