Abstract
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.
Highlights
Emerging robotic technologies that can augment, replace or imitate the functions of human upper limbs are attracting greater attention in the field of industrial manufacturing
We proved the feasibility of noncontact capacitive sensing for human upper-limb motion recognition
One obvious merit of the capacitive sensing method over surface electromyography (sEMG) sensing methods was that it produced accurate motion recognition results with the metal electrodes not being in contact with human skin
Summary
Emerging robotic technologies that can augment, replace or imitate the functions of human upper limbs are attracting greater attention in the field of industrial manufacturing. Robot learning from humans (Billard et al, 2016), which aims to automatically transfer human motor skills to robots, rather than by manual programming, could greatly increase the working efficiency of industrial robotic control. Optical-based methods (Bruno and Khatib, 2016) (cameras, lasers, and depth sensing technologies, etc.) and mechanical sensing methods (Dipietro et al, 2008; Xsens, 20171) (motion capture system, inertial sensing technologies, and data gloves, etc.) are widely used both in academic research and commercial products, as the signals convey abundant human motion information These sensing methods cannot obtain human intent information from muscle contractions
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