Forearm Motion Recognition With Noncontact Capacitive Sensing

• Published in: Frontiers in neurorobotics Vol. 12; p. 47
• Main Authors: Zheng, Enhao, Mai, Jingeng, Liu, Yuxiang, Wang, Qining
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 27.07.2018; Frontiers Media S.A
• Subjects: automatic data labeling; Capacitance; Electrodes; Electromyography; Feasibility studies; Forearm; human-machine interface; Methods; Muscle contraction; Neuroscience; noncontact capacitive sensing; Pattern recognition systems; Researchers; robot learning from humans; Robotics; Robots; Skin; Studies; Ultrasonic imaging; upper-limb motion recognition; Beijing China; China; automatic data labeling; upper-limb motion recognition; noncontact capacitive sensing; robot learning from humans; human-machine interface
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2018.00047

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.