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

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