title: Gesture recognition by instantaneous surface EMG images.
abstract: 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.
authors.affiliation: Zhejiang University, College of Computer Science, Hangzhou, 310027, China.
authors.forename: Weidong
authors.initials: W
authors.lastname: Geng
authors.affiliation: Zhejiang University, College of Computer Science, Hangzhou, 310027, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Du
authors.affiliation: Zhejiang University, College of Computer Science, Hangzhou, 310027, China.
authors.forename: Wenguang
authors.initials: W
authors.lastname: Jin
authors.affiliation: Zhejiang University, College of Computer Science, Hangzhou, 310027, China.
authors.forename: Wentao
authors.initials: W
authors.lastname: Wei
authors.affiliation: Zhejiang University, College of Computer Science, Hangzhou, 310027, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Zhejiang University, College of Computer Science, Hangzhou, 310027, China.
authors.forename: Jiajun
authors.initials: J
authors.lastname: Li
mesh_terms: D004576:Electromyography; D005868:Gestures; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface