Research on lower limb action pattern recognition method based on multi-feature fusion
Existing action pattern recognition methods based on electromyography(EMG)signals have challenges such as insuf-ficient data volume,redundant feature fusion,low classifier recognition accuracy,poor generalization ability,and a limited number of recognized action categories.This study focuses on lower limb movement,collecting surface electromyography(sEMG)signals for four movement categories:walking uphill,walking horizontally,ascending stairs,and descending stairs.This method adopts a feature selection method based on feature correlation and task contribution and finally achieves multi-feature fusion for lower limb action pattern recognition.This method is significantly better than traditional single feature and original signal recognition meth-ods.It provides valuable insights into the study of feature selection and multi-feature fusion in action pattern recognition.
万方数据
维普
