Training-Free Bayesian Self-Adaptive Classification for sEMG Pattern Recognition Including Motion Transition

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 6; pp. 1775 - 1786
• Main Authors: Park, Seongsik, Chung, Wan Kyun, Kim, Keehoon
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activation; Adaptive learning; Algorithms; Bayes methods; Bayesian analysis; Bayesian method; Biomedical measurement; Change detection; Classification; Data collection; Electromyography; Machine learning; Maximum likelihood estimation; Movement; Muscles; Pattern classification; Pattern recognition; Probabilistic methods; Supervised learning; surface electromyogram (sEMG); Training; Transient analysis; unsupervised probabilistic method
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2019.2947089

A direct, ready-to-use surface electromyogram (sEMG) pattern classification algorithm that does not require prerequisite training, regardless of the user, is proposed herein. In addition to data collection, conventional supervised learning approaches for sEMG require labeling and segmenting the data and additional time for the learning algorithm. Consequently, these approaches cannot cope well with sEMG patterns during motion transitions of various movement speeds. The proposed unsupervised and self-adaptive method employs an iterative self-adaptive procedure realized by the probabilistic methods of diffusion, updating, and registration to cluster the activation patterns simultaneously in real time, and classify the current sEMG as new clustered patterns. Experiments demonstrated that even for the same motion, the proposed method could autonomously detect changes in muscular activation patterns varying with the speed of motion. Furthermore, some patterns of both steady- and transient-state motions could be distinguished. In addition, it was verified that the classified sEMG pattern could be correlated consistently with the actual motion, thereby realizing a high level of motion classification.