文献类型：期刊文献

英文题名：Pattern recognition of hand movements based on multi-channel mechanomyography in the condition of one-time collection and sensor doffing and donning

作者：Zhang, Yue[1];Xia, Chunming[2,3];Cao, Gangsheng[2];Zhao, Tongtong[2];Zhao, Yinping[4]

机构：[1]Nantong Univ, Sch Mech Engn, Nantong 226019, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[3]Shanghai Univ Engn Sci, Sch Mech & Automot Engn, Shanghai 201620, Peoples R China;[4]Northwestern Polytech Univ, Sch Software, Xian 710072, Peoples R China

年份：2024

卷号：91

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20240715552747);WOS:【SCI-EXPANDED(收录号:WOS:001186370200001)】；

基金：This work was supported by the Fundamental Research Funds for the Central Universities under Grant G2021KY05106.

语种：英文

外文关键词：Mechanomyography; Hand movements; Pattern recognition; Broad learning system

摘要：Pattern classification of hand movements based on mechanomyography (MMG) has specific application value in the development of human-machine interaction and wearable devices. In the condition of one-time collection, high classification accuracy can be acquired. However, sensor doffing and donning unavoidably change the site and contact pressure of sensors, having a negative effect on classification accuracy. In the condition of sensor doffing and donning, eight-channel MMG of the forearms from participants when they were performing four classes of hand movements were collected for 12 days, and pattern recognition of hand movements were investigated for one-time collection and sensor doffing and donning. After feature extraction and a combination of feature subsets, a broad learning system (BLS) was introduced to pattern recognition of hand movements based on MMG, which was further compared with three other algorithms. In the condition of the one-time collection, recognition rates of each class are higher than 99 %, which is better than that by using a support vector machine (SVM), which demonstrates the excellent learning ability of BLS, whereas the SVM shows a better performance than the BLS in the condition of sensor doffing and donning. When using SVM, BLS, or extreme learning machine, the classification accuracy gradually increases with the number of data subsets, which illustrates that the negative effect on classification accuracy caused by sensor doffing and donning can be alleviated by increasing the amount of training data.