High-Density Surface Electromyogram-based Biometrics for Personal Identification

Abstract

Surface electromyogram (sEMG) has been widely applied in neurorehabilitation techniques such as human-machine 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.Clinical relevance—Our work has proved the huge individual difference of HD-sEMG, which may result from the individually unique bioelectrophysiological activity of human body, deriving from both neural and biomechanical factors. The investigation of subject-specific HD-sEMG pattern may contribute to a better design of subject-specific clinical rehabilitation robots and a deeper understanding of human movement mechanism.