Forearm Motion Recognition With Noncontact Capacitive Sensing

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.

Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications

“…3) The Effect of the Electrode Number: Another factor that contributed to the enhanced inter-day recognition performance was the number of electrodes in the capacitive array. As reported in [32], with six sensing channels, the hand gesture recognition system could only achieve the accuracy of 30.9% in the inter-day experiment. In this study, we downsized the output of capacitive array to investigate the effect of electrode number on the inter-day recognition accuracy.…”

“…Although hand gesture recognition using pressure sensors had been reported in [21]- [24], pressure sensors with large array size are rarely used in gesture recognition tasks. Most of the studies reported wrist-worn and forearm-worn armband with less than ten channels [21]- [23], [32], which were integrated with pressure sensors with low sensitivity. In [24], the authors demonstrated a flexible capacitive wristband with fifteen sensing channels, however, this was still not accurate enough to map the pressure distribution around the wrist.…”

Characterizing Multidimensional Capacitive Servoing for Physical Human-Robot Interaction

“…Non-contact capacitive sensing has also seen applications in controlling prostheses and exoskeletons [21], [22], tracking metallic objects near an industrial robot [23], teleoperation of mobile manipulators [24], and non-contact material recognition [25], [26]. When affixed to the human body as wearable sensors, capacitive proximity sensors have also been used for limb motion recognition [27], activity recognition [28], [29], and health monitoring [30]. In comparison to prior capacitive sensing methods, we characterize a multidimensional capacitive sensor array for physical human-robot interaction, which enables a mobile manipulator to estimate relative human limb pose and perform servoing along the human body.…”

The Rocketbox Library and the Utility of Freely Available Rigged Avatars

“…Another set of techniques uses computer vision to track a person's movement without markers, such as using the Microsoft Kinect sensor (Wei et al, 2012), and more recently RGB cameras (Shiratori et al, 2011;Ahuja et al, 2019), RF reflection (Zhao et al, 2018a,b), and capacitive sensing (Zheng et al, 2018). A final set of techniques uses Worn sensors to recover the user's motion with no external sensors.…”