A Perifacial EMG Acquisition System for Facial-Muscle-Movement Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 21; p. 8758
• Main Authors: Zhang, Jianhang, Huang, Shucheng, Li, Jingting, Wang, Yan, Dong, Zizhao, Wang, Su-Jing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 27-10-2023
• Subjects: Accuracy; backpropagation neural network; Design; Electrodes; Electromyography; Emotions; Experiments; facial-muscle movement; Machine learning; Methods; Physiology; Psychotherapy; random forest; Scandals; Skin; Software; support vector machine; Transmitters; Germany
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23218758

This paper proposes a portable wireless transmission system for the multi-channel acquisition of surface electromyography (EMG) signals. Because EMG signals have great application value in psychotherapy and human–computer interaction, this system is designed to acquire reliable, real-time facial-muscle-movement signals. Electrodes placed on the surface of a facial-muscle source can inhibit facial-muscle movement due to weight, size, etc., and we propose to solve this problem by placing the electrodes at the periphery of the face to acquire the signals. The multi-channel approach allows this system to detect muscle activity in 16 regions simultaneously. Wireless transmission (Wi-Fi) technology is employed to increase the flexibility of portable applications. The sampling rate is 1 KHz and the resolution is 24 bit. To verify the reliability and practicality of this system, we carried out a comparison with a commercial device and achieved a correlation coefficient of more than 70% on the comparison metrics. Next, to test the system’s utility, we placed 16 electrodes around the face for the recognition of five facial movements. Three classifiers, random forest, support vector machine (SVM) and backpropagation neural network (BPNN), were used for the recognition of the five facial movements, in which random forest proved to be practical by achieving a classification accuracy of 91.79%. It is also demonstrated that electrodes placed around the face can still achieve good recognition of facial movements, making the landing of wearable EMG signal-acquisition devices more feasible.