Gesture detection from sEMG signals based on similarity learning

Abstract

Abstract EMG (electromyography) signals provide critical information on muscle activity and have long been used in prosthetics and health care. With the rise of wearable technologies, EMG is now widely applied in human–computer interaction, rehabilitation and emerging remote control applications such as drone operation and gaming. The primary objective of this study was to accurately identify hand gestures utilizing a limited quantity of user electromyography (EMG) data, thereby improving the applicability and extending the potential use cases of EMG-based systems in real-world scenarios. The proposed method makes use of deep learning methods, namely convolutional neural networks and transfer learning to learn the similarity between samples of EMG signals to predict hand gestures. A pretrained network, fine-tuned in multiple passes by progressive freezing of layers, is applied to extract subject-specific features. Support vector machines is used to learn the similarity of the subject’s features to features in the training set to facilitate the voting for the best hand gesture by means of k-nearest neighbors method. The findings indicate viability of the proposed method even when utilizing a limited dataset collected from the user. Average accuracy obtained by the proposed method on 17 test subjects for 7 hand gestures exceeds the performances of the three approaches proposed in two related works as well as an approach based on recurrent neural network. The proposed method was also evaluated on the NinaPro DB5 Exercise B dataset, where it was compared against four existing methods. Experimental results demonstrate that the proposed approach outperforms all compared methods in terms of classification accuracy.