Surface Electromyography Recognition Based on Wavelet Denoising and Time-series Imaging
Surface Electromyography(sEMG)is the signal sent by human muscle contraction,which can well reflect human muscle function,so it is widely used in clinical,prosthesis control,and rehabilitation evaluation etc.However,due to the influence of collec-tor,wearing position,environment and other factors,the signal received by the computer contains random noise,which seriously af-fects the analysis and research of the signal.In this article,we proposed a sEMG recognition method based on a new wavelet thresh-old denosing and time sequence data visualization.Firstly,sEMG of five basic upper limb movements were collected and denoised by improved wavelet decomposition.A new threshold function was proposed to make up for the distortion of soft threshold function and the vibration of hard threshold function in traditional wavelet decomposition,and it was proved that the function was continuous at the threshold and non-deviation from original wavelet coefficient.Then,inspired by the successful application of convolutional neural networks in computer vision,we transformed time-series data into image data using Short-time Fourier Transform.Finally,the experimental results on both the original datasets and the datasets denoised by different methods show that the model obtains su-perior classification results on the datasets denoised by the proposed method.The Two-dimensional Convolutional Neural Networks(2DCNN)model has highest accuracy on four action datasets and second highest accuracy on one action datasets.Therefore,the pro-posed method can effectively improve the recognition rate of sEMG and has good generalization.
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