Optimal Sensor Placement for Motion Tracking of Soft Wearables Using Bayesian Sampling

Abstract

Soft sensors integrated or attached to robots or human bodies enable rapid and accurate estimation of the physical states of the target systems, including position, orientation, and force. While the use of a number of sensors enhances precision and reliability in estimation, it may constrain the movement of the target system or make the entire system complex and bulky. This article proposes a rapid, efficient framework for determining where to place the sensors on the system given the limited number of available sensors. In particular, given $m$ candidates in location for sensor placement, the algorithm recommends $m_0$ locations that guarantee the maximal estimation performance, based on Bayesian sampling. The sampling and optimization method aims to maximize the log-likelihood in nonparametric regression between the measured values of the selected sensors and the target references. The proposed approach for the optimal sensor placement is validated through two scenarios: full-body motion sensing with a soft wearable sensor suit and fingertip position tracking with a motion-capture system. The proposed algorithm successfully determines the sensor locations close to the optimum within 20 min of learning for both cases.