He Jiayuan, Jiang Ning
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Front Bioeng Biotechnol. 2020 Feb 14;8:58. doi: 10.3389/fbioe.2020.00058. eCollection 2020.
Electrical biosignals are favored as biometric traits due to their hidden nature and allowing for liveness detection. This study explored the feasibility of surface electromyogram (sEMG), the electrical manifestation of muscle activities, as a biometric trait. The accurate gesture recognition from sEMG provided a unique advantage over two traditional electrical biosignal traits, electrocardiogram (ECG), and electroencephalogram (EEG), enabling users to customize their own gesture codes. The performance of 16 static wrist and hand gestures was systematically investigated in two identity management modes: verification and identification. The results showed that for a single fixed gesture, using only 0.8-second data, the averaged equal error rate (EER) for verification was 3.5%, and the averaged rank-1 for identification was 90.3%, both comparable to the reported performance of ECG and EEG. The function of customizing gesture code could further improve the verification performance to 1.1% EER. This work demonstrated the potential and effectiveness of sEMG as a biometric trait in user verification and identification, beneficial for the design of future biometric systems.
电生物信号因其隐秘性且可进行活体检测而被视作生物特征。本研究探讨了表面肌电图（sEMG）作为一种生物特征的可行性，表面肌电图是肌肉活动的电表现形式。与两种传统的电生物信号特征——心电图（ECG）和脑电图（EEG）相比，从表面肌电图中准确识别手势具有独特优势，这使得用户能够自定义自己的手势代码。在验证和识别这两种身份管理模式下，系统地研究了16种静态手腕和手部手势的性能。结果表明，对于单个固定手势，仅使用0.8秒的数据，验证的平均等错误率（EER）为3.5%，识别的平均一级识别率为90.3%，两者均与心电图和脑电图的报告性能相当。自定义手势代码的功能可将验证性能进一步提高至EER为1.1%。这项工作证明了表面肌电图作为一种生物特征在用户验证和识别中的潜力和有效性，有利于未来生物识别系统的设计。
