Zheng Enhao, Mai Jingeng, Liu Yuxiang, Wang Qining
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
The Robotics Research Group, College of Engineering, Peking University, Beijing, China.
Front Neurorobot. 2018 Jul 27;12:47. doi: 10.3389/fnbot.2018.00047. eCollection 2018.
This study presents a noncontact capacitive sensing method for forearm motion recognition. A method is proposed to record upper limb motion information from muscle contractions without contact with human skin, compensating for the limitations of existing sEMG-based methods. The sensing front-ends are designed based on human forearm shapes, and the forearm limb shape changes caused by muscle contractions will be represented by capacitance signals. After implementation of the capacitive sensing system, experiments on healthy subjects are conducted to evaluate the effectiveness. Nine motion patterns combined with 16 motion transitions are investigated on seven participants. We also designed an automatic data labeling method based on inertial signals from the measured hand, which greatly accelerated the training procedure. With the capacitive sensing system and the designed recognition algorithm, the method produced an average recognition of over 92%. Correct decisions could be made with approximately a 347-ms delay from the relaxed state to the time point of motion initiation. The confounding factors that affect the performances are also analyzed, including the sliding window length, the motion types and the external disturbances. We found the average accuracy increased to 98.7% when five motion patterns were recognized. The results of the study proved the feasibility and revealed the problems of the noncontact capacitive sensing approach on upper-limb motion sensing and recognition. Future efforts in this direction could be worthwhile for achieving more promising outcomes.
本研究提出了一种用于前臂运动识别的非接触式电容传感方法。提出了一种无需接触人体皮肤就能从肌肉收缩中记录上肢运动信息的方法，弥补了现有基于表面肌电图（sEMG）方法的局限性。传感前端基于人体前臂形状进行设计，肌肉收缩引起的前臂肢体形状变化将由电容信号表示。在实现电容传感系统后，对健康受试者进行实验以评估其有效性。对7名参与者研究了9种运动模式以及16种运动转换。我们还基于测量手部的惯性信号设计了一种自动数据标记方法，这大大加快了训练过程。利用电容传感系统和设计的识别算法，该方法的平均识别率超过92%。从放松状态到运动开始时间点，大约有347毫秒的延迟就能做出正确决策。还分析了影响性能的混杂因素，包括滑动窗口长度、运动类型和外部干扰。我们发现当识别5种运动模式时，平均准确率提高到了98.7%。研究结果证明了该方法的可行性，并揭示了非接触式电容传感方法在上肢运动传感和识别方面存在的问题。在这个方向上未来的努力可能值得期待，以取得更有前景的成果。
