Dynamic Hand Proprioception via a Wearable Glove with Fabric Sensors

Abstract

Continuous enhancement in wearable technologies has led to several innovations in the healthcare, virtual reality, and robotics sectors. One form of wearable technology is wear-able sensors for kinematic measurements of human motion. However, measuring the kinematics of human movement is a challenging problem as wearable sensors need to conform to complex curvatures and deform without limiting the user's natural range of motion. In fine motor activities, such challenges are further exacerbated by the dense packing of several joints, coupled joint motions, and relatively small deformations. This work presents the design, fabrication, and characterization of a thin, breathable sensing glove capable of reconstructing fine motor kinematics. The fabric glove features capacitive sensors made from layers of conductive and dielectric fabrics, culminating in a non-bulky and discrete glove design. This study demonstrates that the glove can reconstruct the joint angles of the wearer with a root mean square error of 7.2 degrees, indicating promising applicability to dynamic pose reconstruction for wearable technology and robot teleoperation.