Towards Inertial Human Motion Tracking with Drift-Free Absolute Orientations using only Sparse Sources of Heading Information

Towards Inertial Human Motion Tracking with Drift-Free Absolute Orientations using only Sparse Sources of Heading Information
Michael Lorenz, Gabriele Bleser, Didier Stricker, Bertram Taetz
Proceedings of the 25th International Conference on Information Fusion in Linköping. International Conference on Information Fusion (FUSION-2022) July 4-7 Linköping Sweden IEEE Explore 2022 .

Abstract:

Tracking a kinematic chain model with inertial sensors and magnetometers using a Bayesian Filter approach typically one magnetometer per segment is used to compensate for a global heading drift. In this work we present a study showing that using an appropriate modeling, heading information can be propagated from one segment to neighboring segments in a kinematic chain. This implies that the amount of required magnetometers can be much lower than one per segment. In particular we elaborate on recent theoretical results and observe that the absolute orientation of all segments in a kinematic chain can be estimated drift-free with only sparse sources of heading information. Our study consists of two parts. The first part is based on a simulated manipulator consisting of three segments. The second one includes the lower body (seven segments) of subjects performing walking trials. Here the inertial sensor data was generated using position and rotation tracking data from a marker-based optical reference system. We show that under certain circumstances the inclusion of a single source of heading information is enough to capture even under disturbances the absolute orientation of the remaining segments drift-free.