Nodes updates censoring and scheduling in constrained decentralized positioning for large-scale motion capture based on wireless body area networks

Abstract

Wireless Body Area Networks (WBAN) are endowed with relatively raw but intrinsic motion capture capabilities through radiolocation, which may be of interest for home activity monitoring, large-scale postural rehabilitation or gaming applications. In this context, we propose a solution to localize wearable wireless nodes relatively to a body-strapped Local Coordinate System (LCS). More particularly, we consider adapting a Constrained Distributed Weighted Multi-Dimensional Scaling algorithm (CDWMDS) that asynchronously estimates nodes' locations under fixed-length constraints. This algorithm is fed by inter-node range measurements based on e.g. Impulse Radio - Ultra Wideband (IR-UWB) Time Of Arrival (TOA) estimation. Several new enhancements to the nominal CDWMDS, including nodes censoring and updates scheduling for nodes' locations, are herein put forward to mitigate errors propagation and harmful effects caused by fast moving nodes. Simulation results are provided to illustrate the gains observed on the average location error per node under moderate pedestrian mobility, relying on a realistic biomechanical model.