Upper Limb Rehabilitation Action Recognition Based on Surface EMG
In this paper,the brachioradialis muscle,ulnar flexor muscle,brachialis muscle,biceps brachialis muscle and deltoid mus-cle of upper limb were selected as the collection objects,and the surface EMG signal was collected by dry electrode and data acquisi-tion card.The time domain features,frequency domain features and information entropy features are extracted after pre-processing the collected surface EMG signals,and the extracted features are used to train the convolutional neural network model.Then some samples of eigenvalues are selected as verification sets for cross-verification.The experimental results show that the training accuracy of fusion information entropy as a feature sample is as high as 91%,which is obviously higher than that of single feature sample and fusion time-frequency domain feature sample.
万方数据
维普
