Original Research ARTICLE
sEMG-based Trunk Compensation Detection in Rehabilitation Training
- 1School of Mechanical and Automotive Engineering, South China University of Technology, China
- 2Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, China
- 3Departement of Rehabilitation Medicine, Third Affiliated Hospital of Sun Yat-Sen University, China
Stroke patients often use trunk to compensate for impaired upper limb motor function during upper limb rehabilitation training, which results in a reduced rehabilitation training effect. Detecting trunk compensations can improve the effect of rehabilitation training. This study investigates the feasibility of a surface electromyography-based trunk compensation detection (sEMG-bTCD) method. Five healthy subjects and nine stroke subjects with cognitive and comprehension skills were recruited to participate in the experiments. The sEMG signals from 9 superficial trunk muscles were collected during three rehabilitation training tasks (reach-forward-back, reach-side-to-side, and reach-up-to-down motions) without compensation and with three common trunk compensations (lean-forward, trunk rotation, and shoulder elevation). Preprocessing like filtering, active segment detection was performed and five time domain features (root mean square, variance, mean absolute value, waveform length, and the 4th order autoregressive model coefficient) were extracted from the collected sEMG signals. Excellent trunk compensation detection performance was achieved in healthy participants by using support vector machine (SVM) classifier (lean-forward: accuracy=94.0%, AUC=0.97, F1=0.94; trunk rotation: accuracy=95.8%, AUC=0.99, F1=0.96; shoulder elevation: accuracy=100.0%, AUC=1.00, F1=1.00). By using SVM classifier, trunk compensation detection performance in stroke participants was also obtained (lean-forward: accuracy=74.8%, AUC=0.90, F1=0.73; trunk rotation: accuracy=67.1%, AUC=0.85, F1=0.71; shoulder elevation: accuracy=91.3%, AUC=0.98, F1=0.90). Compared with the methods based on cameras or inertial sensors, better detection performance was obtained in both healthy and stroke participants. The results demonstrated the feasibility of the sEMG-bTCD method, and it helps to prompt the stroke patients to correct their incorrect posture, thereby improving the effectiveness of rehabilitation training.
Keywords: trunk compensation detection, surface electromyography, Stroke, Rehabilitation training, Support vector machine
Received: 31 May 2019;
Accepted: 05 Nov 2019.
Copyright: © 2019 Ma, Chen, Zhang, Zheng, Yu, Cai and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Longhan Xie, Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China, email@example.com