Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 5; pp. 1071 - 1080
• Main Authors: Ding, Qichuan, Zhao, Xingang, Han, Jianda, Bu, Chunguang, Wu, Chengdong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adaptive classifier; Adult; Algorithms; Classifiers; Discriminant analysis; Electrodes; Electromyography; Electromyography - methods; Electromyography - statistics & numerical data; Fatigue; Female; Hand - innervation; Hand - physiology; Human motion; Humans; Male; Motion; Motion perception; Muscle fatigue; Muscle Fatigue - physiology; Muscles; Muscular fatigue; myoelectric prosthesis; Myoelectricity; online update; Outliers (statistics); Pattern recognition; Pattern Recognition, Automated - methods; Prostheses; Prosthesis Design; Prosthetics; Reproducibility of Results; Robustness; Support Vector Machine; Surface electromyography (sEMG); Therapeutic applications; Training; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2019.2911316

Traditional myoelectric prostheses that employ a static pattern recognition model to identify human movement intention from surface electromyography (sEMG) signals hardly adapt to the changes in the sEMG characteristics caused by interferences from daily activities, which hinders the clinical applications of such prostheses. In this paper, we focus on methods to reduce or eliminate the impacts of three types of daily interferences on myoelectric pattern recognition (MPR), i.e., outlier motion, muscle fatigue, and electrode doffing/donning. We constructed an adaptive incremental hybrid classifier (AIHC) by combining one-class support vector data description and multiclass linear discriminant analysis in conjunction with two specific update schemes. We developed an AIHC-based MPR strategy to improve the robustness of MPR against the three interferences. Extensive experiments on hand-motion recognition were conducted to demonstrate the performance of the proposed method. Experimental results show that the AIHC has significant advantages over non-adaptive classifiers under various interferences, with improvements in the classification accuracy ranging from 7.1% to 39% (p <; 0.01). The additional evaluations on data deviations demonstrate that the AIHC can accommodate large-scale changes in the sEMG characteristics, revealing the potential of the AIHC-based MPR strategy in the development of clinical myoelectric prostheses.