Method for classifying surface EMG signals based on CNN and LSTM

Abstract

The invention discloses a method for classifying surface EMG signals based on CNN and LSTM. The method includes utilizing a sliding window to convert a time sequence into a 'data-tag' pair, applying aHamming window to the surface EMG signals in each time window, using the fast Fourier transform to calculate the time-frequency spectrum Spectrogram, superimposing and integrating the time-sequence spectrum data along the time axis direction, sending the data to a convolutional neural network to complete the local spatial high-level feature extraction and obtain high-dimensional features, expanding the high-dimensional features along the data superposition dimension, restoring the data to the time sequence, feeding the data into a long and short time memory network, extracting the sequence features, sending the sequence features into a fully connected network for further feature extraction and integration to obtain the fully extracted high-dimensional features and feeding the fully extracted high-dimensional features into a Softmax function to get a final classification result. The core of the method is based on a deep learning algorithm, and the classification decoding accuracy is obviously improved by further analysis and extraction on the traditional manual extraction features.

Description

【Technical field】 [0001] The invention belongs to the fields of man-machine interface and elderly care and assistance for the disabled, in particular a surface electromyographic signal classification method based on CNN and LSTM. 【Background technique】 [0002] Surface electromyography is an electrical signal collected by surface electrodes on the skin of the human body. This electrical signal is the potential difference generated by muscle movement near the muscle fibers. When the human body produces a movement intention, the intention is generated in the brain, encoded in the nerve signal and transmitted to the spinal cord, and then transmitted to the corresponding limb (such as the upper limb) through the nerve pathway after the second encoding. The nerve signal causes the muscle fiber to contract and generate a potential difference. Pull the bone to complete the motion. In this process, the motor intention is finally encoded in the electrical signal generated by the mus...

AI Technical Summary

Problems solved by technology

However, the existing classification methods only use traditional hand-extracted features, and there is still room for further improvement in classification accuracy.

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