Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition. (July 2021)

• Record Type: Journal Article
• Authors: Zhou, Yang; Chen, Chaoyang; Cheng, Mark; Alshahrani, Yousef; Franovic, Sreten; Lau, Emily; Xu, Guanghua; Ni, Guoxin; Cavanaugh, John M.; Muh, Stephanie; Lemos, Stephen
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Support vector machine -- Logistic regression -- Artificial neural network -- Pattern recognition -- sEMG -- Shoulder motions; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102577
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Machine learning effectively recognized shoulder motion patterns via multiple sEMG signals. SVM yielded a better accuracy in motion pattern recognition than LR and ANN. The sizes of sliding window in sEMG processing affected recognition accuracy. Increasing the numbers of ANN layer and neuron did not improve recognition accuracy. Abstract: Machine learning (ML) methods have been previously applied and compared in pattern recognition of hand and elbow motions based on surface electromyographic (sEMG) signals. However, there are only a few studies that have investigated the ML methods for shoulder motion pattern recognition. This study compared the efficiency of ML algorithms, including support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) in processing sEMG signals for shoulder motion pattern recognition. This study also investigated the the effects of sliding time window epoch on the recognition accuracy. Eighteen healthy subjects were recruited for this study, their EMG signals were collected from twelve muscles during performing activities of daily living (ADL) motions including drinking, pushing forward/pulling backward, and abduction/adduction. The 80 % of recoded sEMG datasets were used for model training to build the ML models and 20 % were used for model validation and determination of the accuracy of ML algorithms in motion pattern recognition. The influence of sliding time window sizes was studied for algorithm optimization. Statistical analysis was performed to determine the difference in the accuracy of ML methods. Results showed that there was a significant difference among the three machine learning methods and different sliding time window sizes. There was not a significant difference in overlapping time. The highest accuracy was 97.41 ± 1.8 % using the SVM method with a sliding time window of 270 ms. Machine learning techniques provided a quick approach for shoulder motion pattern recognition. The better classifier for pattern recognition of shoulder motion was SVM.