文献类型：期刊文献

英文题名：Muscle synergy analysis of lower limb based on Mechanomyography

作者：Zhang, Hanyang[1]; Cao, Gangsheng[1]; Zhao, Tongtong[1]; Xia, Chunming[1]

机构：[1] East China University of Science and Technology, School of Mechanical Engineering, Shanghai, China

年份：2022

起止页码：366

外文期刊名：Proceedings - 2022 7th International Conference on Communication, Image and Signal Processing, CCISP 2022

收录：EI(收录号：20230313383153)

语种：英文

外文关键词：Activation analysis - Chemical activation - Frequency estimation - Matrix algebra - Non-negative matrix factorization - Patient rehabilitation - Pattern recognition

摘要：Analysis of muscle synergy during the execution of different motions can provide a physiological basis for the rehabilitation assessment of stroke patients. Mechanomyography (MMG) signal is a kind of low-frequency signal produced during muscle vibration, has been widely applied to pattern recognition and muscle fatigue estimation. In this paper, muscle synergy was extracted from 5 channels of MMG signals recorded from 8 healthy subjects in the lower limbs using the non-negative matrix factorization (NNMF) algorithm. In addition, the similarities of muscle activation patterns of 4 different motions were analyzed, and a suitable activation threshold was selected by comparing synergistic and non-synergistic muscles through the coherence analysis method. This study provides a scientific basis for studying muscle synergy based on MMG signals. ? 2022 IEEE.