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Towards Artefact Aware Human Motion Capture using Inertial Sensors Integrated into Loose Clothing

Michael Lorenz; Gabriele Bleser-Taetz; Takayuki Akiyama; Takehiro Niikura; Bertram Taetz; Didier Stricker
In: Proceedings of 2022 IEEE International Conference on Robotics and Automation 2022. IEEE International Conference on Robotics and Automation (ICRA-2022), May 23-27, Philadelphia, PA, USA, IEEE, 5/2022.


Inertial motion capture has become an attractive alternative to optical motion capture for human joint angle estimation outside the laboratory. Usually inertial sensors are assumed to be tightly fixed to the body segments, which can be cumbersome regarding setup-time and ease-of-use. However, integrating the sensors directly into loose clothing, usually, results in additional clothing motion relative to the motion of the underlying bones that should be captured. In this work we propose the Difference Mapping distributions approach that corrects the segment orientations of a given inertial motion capture system that assumes tightly coupled sensors. The approach allows to reduce the joint angle errors due to clothing artefacts by at least 77.2% for people with similar morphology performing a similar task as seen in the training data, including an ergonomic assessments scenario at work places with ten participants. Moreover, we show that the uncertainty of the distribution can be used to measure the reliability of the predicted map if e.g. the motion is further away from the training data to allow for an artefact aware inertial motion tracking approach. The experimental data for this study is available online.