CG-Recognizer: A biosignal-based continuous gesture recognition system

作者：Zheng, Zhiwen; Wang, Qingshan^*; Deng, Dazhu; Wang, Qi; Huang, Wei

来源：Biomedical Signal Processing and Control, 2022, 78: 103995.

DOI：10.1016/j.bspc.2022.103995

• Continuous gesture recognition
• Biological signal processing
• Wearable sensors
• Surface electromyographic signals
• Inertial measurement unit signals
• Deep learning

Gesture is a new communication form of human-computer interaction access because of its abundance and diversity. Continuous gesture recognition based on biosignals has gained widespread attention. However, there are two challenges: the movement epenthesis of continuous gestures leads to the deformations of original gestures, and the different signing speeds among various people lead to the diversity of signal length. To solve them, CG-Recognizer: a biosignal-based continuous gesture recognition system is proposed. To the first challenge, gesture signals are transformed into spectrograms, and a feature generator based on a channelseparated convolutional neural network is constructed to extract the spatio-temporal features of gesture signals. For the second challenge, a standard deviation-based signal segmentation algorithm is first proposed to segment signals and label the features of signals. Then, the labeled signal features are sent to the You Only Look Once version 5 (YOLOv5) model for gesture recognition. The experimental results indicate that the mean accuracy of CG-Recognizer is over 94% on 50 commonly used discrete gestures and over 98% on 40 continuous gestures composed of the above gestures.