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Soft Robotic Glove Controlling Using Brainwave Detection for Continuous Rehabilitation at Home

Talit Jumphoo1, Monthippa Uthansakul1, Pumin Duangmanee1, Naeem Khan2, Peerapong Uthansakul1,*

1 School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
2 Faculty of Electrical and Computer Engineering, University of Engineering and Technology Peshawar, Peshawar, 25000, Pakistan

* Corresponding Author: Peerapong Uthansakul. Email:

Computers, Materials & Continua 2021, 66(1), 961-976.


The patients with brain diseases (e.g., Stroke and Amyotrophic Lateral Sclerosis (ALS)) are often affected by the injury of motor cortex, which causes a muscular weakness. For this reason, they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms. So far, the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices. However, if there is a control system that can directly detect the motor functions, it will induce neuroplasticity to facilitate early motor recovery. In this paper, the control system, which is a motor recovery system with the intent of rehabilitation, focuses on the hand organs and utilizes a brain-computer interface (BCI) technology. The final results depict that the brainwave detection for controlling pneumatic glove in real-time has an accuracy up to 82%. Moreover, the motor recovery system enables the feasibility of brainwave classification from the motor cortex with Arti- ficial Neural Networks (ANN). The overall model performance reveals an accuracy up to 96.56% with sensitivity of 94.22% and specificity of 98.8%. Therefore, the proposed system increases the efficiency of the traditional device control system and tends to provide a better rehabilitation than the traditional physiotherapy alone.


Cite This Article

T. Jumphoo, M. Uthansakul, P. Duangmanee, N. Khan and P. Uthansakul, "Soft robotic glove controlling using brainwave detection for continuous rehabilitation at home," Computers, Materials & Continua, vol. 66, no.1, pp. 961–976, 2021.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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