Abstract
Bending sensors enable compact, wearable designs when used for measuring hand configurations in data gloves. While existing data gloves can accurately measure angular displacement of the finger and distal thumb joints, accurate measurement of thumb carpometacarpal (CMC) joint movements remains challenging due to crosstalk between the multi-sensor outputs required to measure the degrees of freedom (DOF). To properly measure CMC-joint configurations, sensor locations that minimize sensor crosstalk must be identified. This paper presents a novel approach to identifying optimal sensor locations. Three-dimensional hand surface data from ten subjects was collected in multiple thumb postures with varied CMC-joint flexion and abduction angles. For each posture, scanned CMC-joint contours were used to estimate CMC-joint flexion and abduction angles by varying the positions and orientations of two bending sensors. Optimal sensor locations were estimated by the least squares method, which minimized the difference between the true CMC-joint angles and the joint angle estimates. Finally, the resultant optimal sensor locations were experimentally validated. Placing sensors at the optimal locations, CMC-joint angle measurement accuracies improved (flexion, 2.8° ± 1.9°; abduction, 1.9° ± 1.2°). The proposed method for improving the accuracy of the sensing system can be extended to other types of soft wearable measurement devices.
Highlights
In many clinical applications, accurate measurements of hand movements and/or postures are required [1,2]
We developed a new kinematic model of the CMC-joint suitable for the use of flexible bending sensors. Based on this model, using 3D images containing the topology of the skin surface near the CMC-joint, we developed a novel method for estimating the optimal location of two bending sensors that minimizes crosstalk between the sensor outputs
Sensor output was simulated using the radius of curvature and bending angles when the sensor was attached output was simulated using the radius of curvature and bending angles when the sensor was attached at a specific position with a specific orientation on the scanned hand surface
Summary
Accurate measurements of hand movements and/or postures are required [1,2]. A device that can accurately measure different hand postures would be valuable as it enables accurate and precise clinical assessment of hand function and dexterity. In clinical settings, finger- and thumb-joint ranges of motion (ROM) are typically measured using manual goniometers, goniometers are limited to static measurements and typically require a long period of time to collect. A sensing system that allows automatic measurements of hand configuration could significantly improve clinical practice, as it will allow swift measurement of hand postures during static and/or dynamic tasks. An accurate sensing system could improve the performance of hand assistive devices. Recent hand rehabilitation devices enabled compact design through the use of remotely located actuators, which improve usability and convenience [3,4,5]
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