Jiang Xinyu, Xu Ke, Liu Xiangyu, Liu Da, Dai Chenyun, Chen Wei
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:728-731. doi: 10.1109/EMBC44109.2020.9175370.
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.
表面肌电图（sEMG）已广泛应用于人机接口（HMI）等神经康复技术中。长期以来，sEMG特征的个体差异一直是多用户HMI面临的挑战。然而，sEMG独特的个体特性表明其作为一种生物识别方式具有很高的潜力。在这项工作中，我们提出了一种将高密度表面肌电图（HD-sEMG）用于个人身份识别的新应用。除了广泛研究的时间特征外，HD-sEMG还可以解码肌肉激活的高分辨率空间模式，从而提供更充分的信息。我们在22名受试者进行手指肌肉等长收缩时，采集了右手背部的64通道HD-sEMG信号。我们识别每个受试者身份的准确率达到了99.5%，证明了HD-sEMG在个人身份识别方面的卓越性能。据我们所知，这是第一项使用HD-sEMG进行个人身份识别的研究。临床相关性——我们的研究证明了HD-sEMG存在巨大的个体差异，这可能源于人体独特的生物电生理活动，这种活动受神经和生物力学因素的影响。对个体特异性HD-sEMG模式的研究可能有助于更好地设计个体特异性临床康复机器人，并更深入地理解人体运动机制。
