Intelligent Classification of Multi-Gesture EMG Signals Based on LSTM

In the study of prosthetic control technology, researchers usually use decoded electromyographic signals (EMG) to obtain amputees' motion intentions. Predicting and accurately classifying the intent of human gestures can be used not only for rehabilitation and entertainment robots, but also for artificial intelligence robots. It enables us to develop new, more natural methods of human-computer interaction. In this paper, we propose an advanced gesture recognition model. In this model, myoelectric signal of forearm measured by commercial sensor myo is used as input. To extract and classify these gestures, a long-term and short-term memory neural network is applied and compared with other deep learning algorithms. The experimental results show that the model proposed in this paper can recognize the same four gestures as Myo Armband's proprietary recognition system, with an average recognition accuracy of 97.75%, which has higher recognition accuracy than the comparison algorithm. The proposed deep learning model is robust, which means that it can recognize a user's gestures even if there is no user's data in the training data set.