Gesture Recognition and Biometrics Electromyography (GRABMyo) Dataset

Recently, surface electromyogram (EMG) has been proposed as a novel biometric trait for addressing some key limitations of current biometrics, such as spoofing and liveness. The EMG signals possess a unique characteristic: they are inherently different for individuals (biometrics), and they can be customized to realize multi-length codes or passwords (for example, by performing different gestures). However, current EMG-based biometric research has two critical limitations: 1) a small subject pool, compared to other more established biometric traits, and 2) limited to single-session or single-day data sets. In this study, forearm and wrist EMG data were collected from 43 participants over three different days with long separation (Day 1, Day 8 and Day 29) while they performed static hand and wrist gestures. The multi-day biometric authentication resulted in a median EER of 0.017 for the forearm setup and 0.025 for the wrist setup, comparable to well-established biometric traits suggesting consistent performance over multiple days. The presented large-sample multi-day data set and findings could facilitate further research on EMG-based biometrics and other gesture recognition-based applications

近期，表面肌电图（EMG）被提出作为一种新颖的生物特征属性，用以解决当前生物识别技术的一些关键局限性，如欺骗性和真实性的问题。EMG信号具有独特的特性：它们对于个体而言具有固有的差异性（生物特征），并且可以根据需要进行定制，以实现多长度代码或密码（例如，通过执行不同的手势）。然而，基于EMG的生物识别研究目前存在两个关键的局限性：1）与其他更为成熟的生物特征属性相比，样本数量较少，2）局限于单一会话或单日数据集。在本研究中，从43名参与者中收集了前臂和手腕的EMG数据，持续三天，且三天之间有较长的间隔（第1天、第8天和第29天），在他们进行静态手和手腕手势时进行收集。多日生物识别认证结果显示，前臂设置的等错误接受率（EER）中位数为0.017，手腕设置的EER中位数为0.025，与已建立的生物特征属性相当，表明在多个日子中表现一致。所呈现的大样本多日数据集及研究结果，有望促进基于EMG的生物识别技术及其他基于手势识别的应用的进一步研究。