Electromyographic signal gesture recognition method based on deep learning and attention mechanism

Abstract

The invention discloses an electromyographic signal gesture recognition method based on deep learning and attention mechanisms. The method comprises the following steps: performing noise reduction filtering on electromyographic signals; extracting one classic characteristic set from each wind datum by using a sliding window, and establishing a new electromyographic image based on characteristics;designing a deep learning frame based on a convolutional neural network, a circulation neural network and an attention mechanisms, and optimizing network structure parameters of the deep learning frame; performing training with the designed deep learning frame and the training data so as to obtain a classifier model; inputting testing data into the trained deep learning network model, and according to likelihood of a last layer of output, maximally likelihooding corresponding types, that is, recognition types. By adopting the method, electromyographic gesture signals can be recognized on the basis of new characteristic images and deep learning frames based on attention mechanisms. By adopting the electromyographic signal gesture recognition method based on deep learning and attention mechanisms, multiple different gestures of a same subject can be accurately recognized.

Description

technical field [0001] The invention belongs to the field of combining computers and biological signals, and in particular relates to a method for recognizing gestures of myoelectric signals based on deep learning and attention mechanism. Background technique [0002] Surface electromyography (sEMG) is a biological signal that records muscle activity through non-invasive electrodes attached to the skin surface. The recording and analysis of surface electromyographic signals can provide more effective information for assistive and rehabilitation technologies, and has important academic value and application significance for sports science research, human-computer interaction, clinical and basic research of rehabilitation medicine, etc. In these applications, gesture recognition technology based on electromyography plays an important role. A classic EMG gesture recognition process consists of data preprocessing, feature space construction, and classification. The data prepro...

AI Technical Summary

Problems solved by technology

However, there is currently no method of using a cyclic neural network combined with a convolutional neural network to recognize myoelectric signal gestures, and the present invention adds the attention mechanism to the cyclic neural network to enhance the model

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