基于GMM-HMMs与Viterbi回溯的连续手势肌电信号预测与识别OA

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).