Development of wearable sensing suit for monitoring wrist joint motions and deep neural

Abstract

A measurement of a users' motion is widely attracting attention for a realization of robotic assistance in daily activities. The soft, wearable sensing suit enables the monitoring of outdoor activities, with high wearability and insensitivity to inertial force. In this paper, we propose a novel sensing suit for measuring the multi degree of freedom (multi-DOF) motion of the wrist joints. We used a fabric-based capacitance-type stretch sensor for high adaptivity to a textile form of suits. The sensor was attached to the body link, instead of the wrist joint to reduce the interdependency among each joint axis and the effect of unwanted disturbance. We adopted the Deep Neural Network for calibration, and verified the higher estimation accuracy on the estimation of the multi-DOF wrist motions. The performance validation proceeded with comparing to the linear-based regression, and the root mean-squared error on the angle measurement was improved at slow motion and fast motion. A real-time measurement interface was developed and demonstrated with a frequency of 250 Hz.