Ambulatory inertial spinal tracking using constraints

Markus Miezal; Bertram Taetz; Norbert Schmitz; Gabriele Bleser-Taetz

In: Proceedings of the 9th International Conference on Body Area Networks. International Conference on Body Area Networks (Bodynets-09), October 29 - November 1, London, United Kingdom, 9/2014.


Wearable inertial sensor networks represent a well-known and meanwhile cheap solution for in-field motion capturing. However, the majority of existing approaches and products rely on simple stick figure models to approximate the hu- man skeleton with only a few rigid segments and connecting joints. Especially the spine is often extremely simplified with one or at most two segments. This simplification results in significant kinematic estimation errors. This paper presents a novel inertial tracking approach, where a recursive filter with integrated constraints enables detailed and efficient es- timation of the spine kinematics in real time. The advan- tages of the proposed approach are confirmed in experiments using ground truth data from an optical reference system.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz