Zheng Enhao, Wan Jiacheng, Xu Dongfang, Wang Qining, Qiao Hong
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4109-4113. doi: 10.1109/EMBC44109.2020.9175438.
Human-machine interface with muscle signals serves as an important role in the field of wearable robotics. To compensate for the limitations of the existing surface Electromyography (sEMG) based technologies, we previously proposed a noncontact capacitive sensing approach that could record the limb shape changes. The sensing approach frees the human skin from contacting to the metal electrodes, thus enabling the measurement of muscle signals by dressing the sensing front-ends outside of the clothes. We validated the capacitive sensing in human motion intent recognition tasks with the wearable robots and produced comparable results to existing studies. However, the biological significance of the capacitance signals is still unrevealed, which is an indispensable issue for robot intuitive control. In this study, we address the problems of identifying the relationships between the muscle morphological parameters and the capacitance signals. We constructed a measurement system that recorded the noncon-tact capacitive sensing signals and the muscle ultrasound (US) images simultaneously. With the designed device, five subjects were employed and the US images from the gastrocnemius muscle (GM) and the tibialis anterior (TA) muscle during level walking were sampled. We fitted the calculated muscle morphological parameters (the pinnation angles and the muscle fascicle length) and the capacitance signals of the same gait phases. The results demonstrated that at least one-channel capacitance signal strongly correlated to the muscle morphological parameters (R > 0.5, quadratic regression). The average Rs of the most correlated channels were up to 0.86 for pinnation angles and 0.83 for the muscle fascicle length changes. The interesting findings in this preliminary study suggest the biological physical significance of the capacitance signals during human locomotion. Future efforts are worth being paid in this new research direction for more promising results.
基于肌肉信号的人机界面在可穿戴机器人领域发挥着重要作用。为了弥补现有基于表面肌电图（sEMG）技术的局限性，我们之前提出了一种非接触式电容传感方法，该方法可以记录肢体形状的变化。这种传感方法使人体皮肤无需与金属电极接触，从而能够通过将传感前端穿戴在衣服外面来测量肌肉信号。我们在可穿戴机器人的人体运动意图识别任务中验证了电容传感，并产生了与现有研究相当的结果。然而，电容信号的生物学意义仍未揭示，这对于机器人的直观控制是一个不可或缺的问题。在本研究中，我们解决了识别肌肉形态参数与电容信号之间关系的问题。我们构建了一个测量系统，该系统同时记录非接触式电容传感信号和肌肉超声（US）图像。使用设计的设备，招募了五名受试者，并采集了他们在平地行走时腓肠肌（GM）和胫骨前肌（TA）的US图像。我们拟合了计算出的肌肉形态参数（羽状角和肌肉束长度）与相同步态阶段的电容信号。结果表明，至少一个通道的电容信号与肌肉形态参数密切相关（R>0.5，二次回归）。对于羽状角，最相关通道的平均R值高达0.86，对于肌肉束长度变化，平均R值高达0.83。这项初步研究中的有趣发现表明了人体运动过程中电容信号的生物物理意义。在这个新的研究方向上值得付出更多努力以获得更有前景的结果。
