An upper limb movement estimation from electromyography by using BP neural network

Abstract

Abstract The body electromyography (EMG) signals contain a large amount of information related to the movement of the human body. Identifying the patients’ movement intention from the EMG signals is the key to controlling the exoskeleton to assist their movement. In order to accurately extract the information about the patients’ movement intention from the EMG signals, we preprocessed the EMG signals including signals amplification, denoising, biasing and normalization. Then we extracted the features of EMG signals from the time domain, frequency domain, and time-frequency domain respectively. Based on the features obtained, we used the Matlab neural network toolbox to train BP neural network and tested the established continuous movement control model. The results suggested that the angles estimated by the continuous movement control model had smaller errors. In addition, instead of the traditional working mode that used the PC to process the EMG signals, we used the STM32 microcontroller to perform real-time control of the upper limb exoskeleton, which greatly reduced the size of the control equipment and provided convenience for the patients’ rehabilitation training.