Forearm sEMG from hand and wrist gestures

Recently, surface electromyography (sEMG) emerged as a novel biometric authentication method. Since EMG system parameters, such as the feature extraction methods and the number of channels, have been known to affect system performances, it is important to investigate these effects on the performance of the sEMG-based biometric system to determine optimal system parameters. In this study, three robust feature extraction methods, Time-domain (TD) feature, Frequency Division Technique (FDT), and Autoregressive (AR) feature, and their combinations were investigated while the number of channels varying from one to eight. For these system parameters, the performance of sixteen static wrist and hand gestures was systematically investigated in two authentication modes: verification and identification. The results from 24 participants showed that the TD features significantly (p

近期，表面肌电图（sEMG）作为一种新颖的生物识别认证方法崭露头角。鉴于肌电图系统参数，如特征提取方法和通道数量，已被证实会影响到系统性能，因此，探究这些因素对基于sEMG生物识别系统性能的影响，以确定最佳系统参数，显得尤为重要。在本研究中，针对时间域（TD）特征、频分技术（FDT）和自回归（AR）特征三种稳健的特征提取方法及其组合进行了调查，同时考察了从单通道至八通道的通道数量变化。针对这些系统参数，对十六种静态手腕和手部手势在两种认证模式下的性能进行了系统性的研究：验证和识别。来自24位参与者的研究结果揭示了TD特征在显著性水平上对系统性能产生了显著影响（p值未给出）。