SVM-Based Classification of sEMG Signals for Upper-Limb Self-Rehabilitation Training - Cai, Siqi; Chen, Yan; Huang, Shuangyuan; Wu, Yan; Zheng, Haiqing; Li, Xin; Xie, Longhan (Frontiers in Neurorobotics, 2019)
Robot-assisted rehabilitation is a growing field that can provide an intensity, quality and quantity of treatment that exceeds therapist-mediated rehabilitation. Several control algorithms have been implemented in rehabilitation robots to develop a patient-cooperative strategy with the capacity to understand the intention of the user and provide suitable rehabilitation training. In this paper, we present an upper-limb motion pattern recognition method using surface electromyography (sEMG) signals with a support vector machine (SVM) to control a rehabilitation robot ReRobot, which was built to conduct upper-limb rehabilitation training for poststroke patients. For poststroke rehabilitation training using the ReRobot, the upper-limb motion of the patient’s healthy side is first recognized by detecting and processing the sEMG signals, then the ReRobot assists the impaired arm in conducting mirror rehabilitation therapy. To train and test the SVM model, five healthy subjects participated in the experiments, and performed five standard upper-limb motions, including shoulder flexion, abduction, internal rotation, external rotation and elbow joint flexion. Good accuracy was demonstrated in experimental results from the five healthy subjects. By recognizing the model motion of the healthy side, the rehabilitation robot can provide mirror therapy to the affected side. This method can be used as a control strategy of upper-limb rehabilitation robots for self-rehabilitation training with stroke patients.