Abstract
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.
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