Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation

Abstract

In this paper, we present an online method for the unsupervised segmentation of skeletal motion capture data for the assessment of unfavorable or harmful postures in the context of musculoskeletal disorders. The long-time motion capture data is segmented into short motion sequences using joint distances of the captured skeleton. We use the difference between joint distance matrices to detect variances in motion dynamics in which the motion is separated into either a dynamic motion or a static posture. Then, the static posture can be evaluated using well-known posture assessment methods such as the Ovako Working postures Analysing System (OWAS) to derive risk factors for musculoskeletal disorders. The algorithm works in real-time so that it can be incorporated in live warning systems for unfavorable or harmful postures. We evaluated the segmentation algorithm by comparing it with results from state-of-the-art offline motion segmentation algorithms as gold standard. Results show that the algorithm approaches the performance of state-of-the-art offline segmentation algorithms.

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Bibtex:

@INPROCEEDINGS{Lins2018motion,
author={C. Lins, S. M. Müller, M. Pfingsthorn, M. Eichelberg, A. Gerka, A. Hein},
booktitle={11th International Conference on Health Informatics (Healthinf 2018)},
title={Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation},
year={2018},
month={Jan.},}