文献类型：期刊文献

英文题名：Measuring Neuromuscular Electrophysiological Activities to Decode HD-sEMG Biometrics for Cross-Application Discrepant Personal Identification With Unknown Identities

作者：Jiang, Xinyu[1];Liu, Xiangyu[2];Fan, Jiahao[1,3,4];Ye, Xinming[5];Dai, Chenyun[1];Clancy, Edward A.[6];Chen, Wei[1,3,4]

机构：[1]Fudan Univ, Ctr Intelligent Med Elect, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China;[2]Univ Shanghai Sci & Technol, Coll Commun & Art Design, Shanghai 200093, Peoples R China;[3]Fudan Univ, Human Phenome Inst, Shanghai 201210, Peoples R China;[4]Fudan Univ, Zhangjiang Fudan Int Innovat Ctr, Shanghai 201210, Peoples R China;[5]East China Univ Sci & Technol, Sch Sports Sci & Engn, Shanghai 200237, Peoples R China;[6]Worcester Polytech Inst, Dept Elect & Comp Engn, Worcester, MA 01609 USA

年份：2022

卷号：71

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20222412226641);WOS:【SCI-EXPANDED(收录号:WOS:000875898900021)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62173094 and in part by the Shanghai Municipal Science and Technology Project under Grant 20510710500.

语种：英文

外文关键词：Biometrics; high-density surface electromyogram (HD-sEMG); user identification

摘要：Measuring the physical, physiological, behavioral, or chemical characteristics of an individual as biometrics for personal identification has attracted increasing attention in smart environment applications. Noncancelability and cross-application invariance are two flaws of traditional DNA, face, and fingerprint-based biometrics because users cannot volitionally change the biometric template. In this work, we acquired high-density surface electromyogram (HD-sEMG) signals encoded by gesture passwords as biometrics. The different sEMG patterns under different motor tasks allow users to enroll multiple accounts using sEMG under different hand gestures as biometrics. By simply changing to a new gesture password, users can cancel the original template once it is compromised. Even if impostors enter the correct gesture password, the individual differences of HD-sEMG as the second defense can still achieve excellent performance. To improve the current state-of-the-art identification accuracy, we acquired 256-channel forearm HD-sEMG and decoded high-resolution neuromuscular information in temporal-spectral-spatial domain. We achieved a high identification accuracy of 99.85% on a 200-account (20 subjects x 10 accounts per subject) recognition task, with training and testing data acquired 3 to 25 days apart. Moreover, to address the concern of "unknown identities," we applied an "authentication + identification" validation, achieving high accuracy of 93.81% on a 200-account [(16 enrolled subjects + 4 unknown subjects) x 10 accounts per subject] task. Our work substantially improves the current state-of-the-art accuracy for cross-day sEMG biometric identification (improved from similar to 88% to >99% with a similar number of identified classes).