南京信息工程大学学报2026，Vol.18Issue(1)：11-17,7.DOI:10.13878/j.cnki.jnuist.20240905002
基于GMM-HMMs与Viterbi回溯的连续手势肌电信号预测与识别
Consecutive gesture prediction and recognition from sEMG using GMM-HMMs and Viterbi backtracking
摘要
Abstract
Addressing the issues of poor real-time performance and insufficient prediction capability in consecutive gesture recognition tasks based on surface Electromyography(sEMG),we propose an approach utilizing Gaussian Mixture Model-Hidden Markov Models(GMM-HMMs)and Viterbi backtracking.This approach leverages sliding window technique to segment the 8-channel sEMG signals,and GMM-HMMs to classify hand gestures into 4 action states:idle,ascending,steady,and descending.A refined Viterbi sliding window marginalization strategy is imple-mented to ensure prolonged connections between adjacent windows,enabling anticipatory prediction of subsequent gesture states.Moreover,a dynamic threshold model based on maximum likelihood is incorporated to accurately cate-gorize gestures.In a task involving 12 consecutive two-gesture sequences completed by 8 participants,the proposed approach attained an average recognition rate of 98.1%with a prediction time of 71 ms,significantly outperforming both the LSTM model(94.2%,309 ms)and the GRU model(93.8%,300 ms).关键词
模式识别/连续手势/GMM-HMMs/Viterbi回溯/表面肌电信号Key words
pattern recognition/consecutive gesture/Gaussian mixture model-hidden markov models(GMM-HMMs)/Viterbi backtracking/sEMG分类
信息技术与安全科学引用本文复制引用
杨进兴,刘帅,李俊..基于GMM-HMMs与Viterbi回溯的连续手势肌电信号预测与识别[J].南京信息工程大学学报,2026,18(1):11-17,7.基金项目
福建省科技计划(2022L3094) （2022L3094）
泉州市科技计划(2021C021R) （2021C021R）
黎明职业大学2024年度规划项目(LZ202406) （LZ202406）
