Paper Info
Title
High-Density Surface Electromyogram-Based Biometrics For Personal Identification
Abstract
Surface electromyogram (sEMG) has been widely applied in neurorehabilitation techniques such as humanmachine interface (HMI). The individual difference of sEMG characteristics has long been a challenge for multi-user HMI. However, the individually unique sEMG property indicates its high potential as a biometrics modality. In this work, we propose a novel application of high-density sEMG (HD-sEMG) for personal identification. HD-sEMG can decode the high-resolution spatial patterns of muscle activations, besides the widely studied temporal features, thus providing more sufficient information. We acquired 64-channel HD-sEMG signals on the dorsum of the right hand from 22 subjects during finger muscle isometric contractions. We achieved an accuracy of 99.5% to recognize the identity of each subject, demonstrating the excellent performance of HD-sEMG for personal identification. To the best of our knowledge, this is the first study to employ HD-sEMG for personal identification.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9175370
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
DocType
Volume
ISSN
Conference
2020
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Xinyu Jiang188.27
Ke Xu21392171.73
Xiangyu Liu332.73
D. Liu427133.37
Chenyun Dai501.69
Wei Chen69639.08