Li Junwei, Wu Kunlin, Xiao Jingcheng, Chen Tianyu, Yang Xudong, Pan Jie, Chen Yu, Wang Yifan
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore, Singapore.
Sci Adv. 2025 Jun 27;11(26):eadv3359. doi: 10.1126/sciadv.adv3359.
The demand for advanced human-machine interfaces (HMIs) highlights the need for accurate measurement of muscle contraction states. Traditional methods, such as electromyography, cannot measure passive muscle contraction states, while optical and ultrasonic techniques suffer from motion artifacts due to their rigid transducers. To overcome these limitations, we developed a flexible multichannel electrical impedance sensor (FMEIS) for noninvasive detection of skeletal muscle contractions. By applying an imperceptible current, the FMEIS can target multiple deep muscles by capturing electric-field ripples generated by their contractions. With an ultrathin profile (~220 micrometers), a low elastic modulus (212.8 kilopascals) closely matching human skin, and engineered adhesive sensor surfaces, the FMEIS conforms nicely to human skin with minimized motion artifacts. The FMEIS achieved high accuracy in both hand gesture recognition and muscle force prediction using machine learning models. With demonstrated performance across multiple HMI applications, including human-robot collaboration, exoskeleton control, and virtual surgery, FMEIS shows great potential for future real-time collaborative HMI systems.
对先进人机界面（HMI）的需求凸显了准确测量肌肉收缩状态的必要性。传统方法，如肌电图，无法测量被动肌肉收缩状态，而光学和超声技术由于其刚性换能器会受到运动伪影的影响。为克服这些限制，我们开发了一种用于无创检测骨骼肌收缩的柔性多通道电阻抗传感器（FMEIS）。通过施加不易察觉的电流，FMEIS能够通过捕捉由肌肉收缩产生的电场波动来针对多个深层肌肉。FMEIS具有超薄外形（约220微米）、与人类皮肤紧密匹配的低弹性模量（212.8千帕）以及经过设计的粘性传感器表面，能很好地贴合人类皮肤，将运动伪影降至最低。利用机器学习模型，FMEIS在手势识别和肌肉力量预测方面均实现了高精度。FMEIS在包括人机协作、外骨骼控制和虚拟手术在内的多个HMI应用中展现出了性能，在未来实时协作HMI系统中具有巨大潜力。
