sEMG-Based Trunk Compensation Detection in Rehabilitation Training

Ke Ma; Yan Chen; Xiaoya Zhang; Haiqing Zheng; Song Yu; Siqi Cai; Longhan Xie

doi:10.3389/fnins.2019.01250

sEMG-Based Trunk Compensation Detection in Rehabilitation Training

Front Neurosci. 2019 Nov 21:13:1250. doi: 10.3389/fnins.2019.01250. eCollection 2019.

Authors

Ke Ma¹, Yan Chen², Xiaoya Zhang³, Haiqing Zheng³, Song Yu², Siqi Cai², Longhan Xie²

Affiliations

¹ School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
² Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China.
³ Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Abstract

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 nine 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 (LF), trunk rotation (TR), and shoulder elevation (SE)]. Preprocessing like filtering, active segment detection was performed and five time domain features (root mean square, variance, mean absolute value (MAV), waveform length, and the fourth order autoregressive model coefficient) were extracted from the collected sEMG signals. Excellent TCD performance was achieved in healthy participants by using support vector machine (SVM) classifier (LF: accuracy = 94.0%, AUC = 0.97, F1 = 0.94; TR: accuracy = 95.8%, AUC = 0.99, F1 = 0.96; SE: accuracy = 100.0%, AUC = 1.00, F1 = 1.00). By using SVM classifier, TCD performance in stroke participants was also obtained (LF: accuracy = 74.8%, AUC = 0.90, F1 = 0.73; TR: accuracy = 67.1%, AUC = 0.85, F1 = 0.71; SE: 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: rehabilitation training; stroke; support vector machine; surface electromyography; trunk compensation detection.