Multi-Channel sEMG Signal Gesture Recognition Based on Improved CNN-LSTM Hybrid Models
Dianchun Bai, Tie Liu, Xinghua Han, Guo Chen, Yinlai Jiang, Yokoi Hiroshi
Abstract
Deep learning gesture recognition based on surface electromyography (sEMG) is playing an increasingly important role in prosthetic hand control. In order to improve the recognition rate of multi-modal EMG signals, this paper proposes a feature model construction and optimization method based on multi-channel EMG signal amplification unit. And through CNN and LSTM (CNN+LSTM) deep learning model, the recognition rate and acquisition window are trained. Use the established time series surface EMG image to construct a feature model to solve the recognition problem of multi-modal surface EMG signal. The experimental results show that under the same network structure, the EMG signal processed by Fast Fourier Transform (FFT) as the characteristic value has better performance.