Tracking Human Pose During Robot-Assisted Dressing using Single-Axis Capacitive Proximity Sensing

TL;DR

This paper introduces a real-time human pose tracking method using capacitive proximity sensing for robot-assisted dressing, enabling robots to adapt to human motion despite occlusions and pose estimation errors.

Contribution

The novel use of capacitive proximity sensing for real-time human pose tracking during dressing tasks, overcoming limitations of vision-based methods.

Findings

Successfully dressed 10 participants with arm motions and pose errors

Capacitive sensors are unaffected by clothing and occlusion

Enables robots to adapt to human movement in real time

Abstract

Dressing is a fundamental task of everyday living and robots offer an opportunity to assist people with motor impairments. While several robotic systems have explored robot-assisted dressing, few have considered how a robot can manage errors in human pose estimation, or adapt to human motion in real time during dressing assistance. In addition, estimating pose changes due to human motion can be challenging with vision-based techniques since dressing is often intended to visually occlude the body with clothing. We present a method to track a person's pose in real time using capacitive proximity sensing. This sensing approach gives direct estimates of distance with low latency, has a high signal-to-noise ratio, and has low computational requirements. Using our method, a robot can adjust for errors in the estimated pose of a person and physically follow the contours and movements of the person while providing dressing assistance. As part of an evaluation of our method, the robot successfully pulled the sleeve of a hospital gown and a cardigan onto the right arms of 10 human participants, despite arm motions and large errors in the initially estimated pose of the person's arm. We also show that a capacitive sensor is unaffected by visual occlusion of the body and can sense a person's body through cotton clothing.