Research on the continuous motion prediction method of finger joint angles using dual-stream CNN based on feature map combination
To address the insufficient extraction of timing information and low accuracy in predicting continuous motion of finger joint angles based on surface electromyographic(sEMG)signals,we propose a two-stream convolutional neural network prediction method based on feature map combination(FMC).First,the feature information of the sEMG signal is extracted.Then,the feature information is integrated into feature maps(FMC)by employing a sliding window method to express the temporal coherence of the features and extract the temporal information of the sEMG signal.Finally,the dual stream convolutional neural network(DCNN)network is used to extract deep features from the combined feature maps in the temporal and spatial dimensions to improve the prediction of finger joint angles continuous motion.Experiments are conducted on the NinaPro-DB8 dataset,and our results show,compared with three different degrees of freedom(18,5,3),the R2 values of healthy subjects increase by 7.9％,16.8％,and 17.8％respectively,while the R2 values of amputees increase by 9.6％,14.3％,and 10.3％respectively.
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