Recognition of Electromyographic Signal Time Series on Daily Hand Motions Based on Long Short-Term Memory Network

Received: 15 November 2020 Accepted: 4 February 2021 Electromyographic (EMG) signals contain various information about muscle actions, such as intensity and time. In most studies on EMG signals of hand motions, the model is trained on a single action dataset before being applied to action recognition. But rehabilitation training aims to enable the patient to make daily actions, each of which encompasses a series of individual actions. Because EMG signals are noisy and non-stationary, the transition between individual actions could result in a high error in action recognition. This paper designs the common composite actions in daily life, and then obtains the EMG signal data of continuous actions. The signal segments of the composite actions were determined to derive the static and dynamic states of each action. With the aid of the sliding window, the authors obtained the time series data of the eight-channel EMG signals of each composite action, which vary with the elapse of time. Then, the time series data were transformed into the time-frequency image flow of the corresponding composite action. The ten individual actions of the designed composite hand actions were recognized by a self-designed model, which couples three-dimensional convolutional neural network (3D CNN) with long shortterm memory (LSTM) network. The recognition rate was as high as 92%. Finally, an interactive simulation environment was constructed for hand actions on Unity5.3.1f1. Under the environment, the accuracy of controlling the movement of an unmanned vehicle with hand actions was measured. The results show that the recognition rate of our method stabilized at 90%.