Motion Judgment Experiment by Using Surface Electromyography and Three-axis Information Fusion
In order to improve the accuracy of motion recognition based on surface electromyography and three-axis acceleration signals,a set of experimental procedures and methods for multi-source information fusion processing are proposed.Firstly,this experimental method uses five-layer discrete wavelet transform to decompose the surface electromyographic signal and fully extract the characteristic information of each frequency domain in the electromyographic signal generated by different movements.Secondly,the decomposed surface electromyographic signal and the three-axis acceleration signal are combined by sliding window method and construct a feature map that fuses electromyographic and spatial motion features.Finally,the fused features map are used to train the deep learning model,and the trained model is combined with an automatic state machine to identify the final motion state.Experimental results show that the multi-source information fusion processing method can improve the accuracy of motion recognition,with the overall recognition accuracy reaching 95.4％and 89.2％respectively.It has good performance in real-time and accuracy.
multi-source information fusionsurface electromyography signalmotion recognitiontime-frequency analysisdeep learning
万方数据
维普
