Open Access
ARTICLE
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. https://doi.org/10.32604/cmc.2020.012433
Received 30 June 2020; Accepted 14 September 2020; Issue published 30 October 2020
Abstract
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.
Keywords
Cite This Article
APA Style
Jumphoo, T., Uthansakul, M., Duangmanee, P., Khan, N., Uthansakul, P. (2021). Soft robotic glove controlling using brainwave detection for continuous rehabilitation at home. Computers, Materials & Continua, 66(1), 961-976. https://doi.org/10.32604/cmc.2020.012433
Vancouver Style
Jumphoo T, Uthansakul M, Duangmanee P, Khan N, Uthansakul P. Soft robotic glove controlling using brainwave detection for continuous rehabilitation at home. Comput Mater Contin. 2021;66(1):961-976 https://doi.org/10.32604/cmc.2020.012433
IEEE Style
T. Jumphoo, M. Uthansakul, P. Duangmanee, N. Khan, and P. Uthansakul "Soft Robotic Glove Controlling Using Brainwave Detection for Continuous Rehabilitation at Home," Comput. Mater. Contin., vol. 66, no. 1, pp. 961-976. 2021. https://doi.org/10.32604/cmc.2020.012433