文献类型：期刊文献

英文题名：Generalized Finger Motion Classification Model Based on Motor Unit Voting

作者：Liu, Xiangyu[1];Zhou, Meiyu[1];Dai, Chenyun[2];Chen, Wei[2];Ye, Xinming[3]

机构：[1]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai, Peoples R China;[2]Fudan Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China;[3]East China Univ Sci & Technol, Sch Sports Sci & Engn, Shanghai, Peoples R China

年份：2021

卷号：25

期号：1

起止页码：100

外文期刊名：MOTOR CONTROL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000601288200008)】；

基金：This research was supported by Shanghai Artistic Science Program (ZD2018F01).

语种：英文

外文关键词：finger pattern recognition; HD-sEMG; human-machine interface; neural prosthesis control

摘要：Surface electromyogram-based finger motion classification has shown its potential for prosthetic control. However, most current finger motion classification models are subject-specific, requiring calibration when applied to new subjects. Generalized subject-nonspecific models are essential for real-world applications. In this study, the authors developed a subject-nonspecific model based on motor unit (MU) voting. A high-density surface electromyogram was first decomposed into individual MUs. The features extracted from each MU were then fed into a random forest classifier to obtain the finger label (primary prediction). The final prediction was selected by voting for all primary predictions provided by the decomposed MUs. Experiments conducted on 14 subjects demonstrated that our method significantly outperformed traditional methods in the context of subject-nonspecific finger motion classification models.