RESEARCH ON GAIT RECOGNITION OF SURFACE EMG SIGNAL BASED ON MPSO-LSTM ALGORITHM

被引:0
|
作者
Chang, Ying [1 ,2 ]
Wang, Lan [1 ]
Li, Min [3 ]
Liu, Ming [4 ]
Lin, Lingjie [1 ]
Cui, Bo [2 ]
Liu, Qimeng [2 ]
机构
[1] Harbin Engn Univ, Coll Mech & Elect Engn, Harbin 150006, Peoples R China
[2] Jilin Agr Sci & Technol Univ, Sch Mech & Civil Engn, Jilin 132109, Peoples R China
[3] JiLin Cent Hosp, Resp Dept, Jilin 132109, Peoples R China
[4] Suzhou Mizuho Machinery Co Ltd, Suzhou 215000, Peoples R China
关键词
sEMG; MPSO-LSTM; motion recognition; data acquisition;
D O I
10.1142/S0219519423400663
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
The growing interest in gait recognition based on surface electromyography (sEMG) signals is attributed to their capability to anticipate motion characteristics during human movement. This paper focuses on gait pattern recognition using sEMG signals. Initially, the muscles responsible for collecting sEMG signals are determined based on the distinct characteristics of human gait, and data for 12 different gait patterns are collected. Subsequently, the acquired sEMG signals undergo preprocessing and feature extraction stages. Moreover, various algorithms relevant to gait classification based on surface myoelectric signals are investigated. In this study, we propose an improved particle swarm optimization algorithm (MPSO-LSTM) for accurately classifying gait patterns using surface myoelectric signals. Experimental results demonstrate the effectiveness of the MPSO-LSTM algorithm in gait recognition based on sEMG signals.
引用
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页数:19
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