Gesture recognition by instantaneous surface EMG images

• Published in: Scientific reports Vol. 6; no. 1; p. 36571
• Main Authors: Geng, Weidong, Du, Yu, Jin, Wenguang, Wei, Wentao, Hu, Yu, Li, Jiajun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.11.2016; Nature Publishing Group
• Subjects: 631/114/1314; 631/114/2397; Algorithms; Classification; Computer applications; Electrodes; Electromyography; Experiments; Humanities and Social Sciences; Interfaces; Latency; multidisciplinary; Prosthetics; Science
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep36571

Gesture recognition in non-intrusive muscle-computer interfaces is usually based on windowed descriptive and discriminatory surface electromyography (sEMG) features because the recorded amplitude of a myoelectric signal may rapidly fluctuate between voltages above and below zero. Here, we present that the patterns inside the instantaneous values of high-density sEMG enables gesture recognition to be performed merely with sEMG signals at a specific instant. We introduce the concept of an sEMG image spatially composed from high-density sEMG and verify our findings from a computational perspective with experiments on gesture recognition based on sEMG images with a classification scheme of a deep convolutional network. Without any windowed features, the resultant recognition accuracy of an 8-gesture within-subject test reached 89.3% on a single frame of sEMG image and reached 99.0% using simple majority voting over 40 frames with a 1,000 Hz sampling rate. Experiments on the recognition of 52 gestures of NinaPro database and 27 gestures of CSL-HDEMG database also validated that our approach outperforms state-of-the-arts methods. Our findings are a starting point for the development of more fluid and natural muscle-computer interfaces with very little observational latency. For example, active prostheses and exoskeletons based on high-density electrodes could be controlled with instantaneous responses.