Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation

Christian Lins; Sebastian M. Müller; Max Pfingsthorn; Marco Eichelberg; Alexander Gerka; Andreas Hein

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Paper

Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation

Topics: Development of Assistive Technology; Medical Informatics; Pattern Recognition and Machine Learning; Wearable Health Informatics

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: BIOSTEC, 478-485, 2018 , Funchal, Madeira, Portugal

Authors: Christian Lins ¹ ; Sebastian M. Müller ² ; Max Pfingsthorn ² ; Marco Eichelberg ² ; Alexander Gerka ² and Andreas Hein ¹

Affiliations: ¹ Carl von Ossietzky University, Germany ; ² OFFIS - Institute for Information Technology, Germany

Keyword(s): Human Motion Analysis, Temporal Segmentation, Joint Distance Matrices, Musculoskeletal Disorders, Ergonomics Assessment.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Biomedical Signal Processing ; Development of Assistive Technology ; Devices ; Health Information Systems ; Human-Computer Interaction ; Pattern Recognition and Machine Learning ; Physiological Computing Systems ; Wearable Sensors and Systems

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 t he algorithm approaches the performance of state-of-the-art offline segmentation algorithms. (More)

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Paper citation in several formats:

Lins, C.; Müller, S.; Pfingsthorn, M.; Eichelberg, M.; Gerka, A. and Hein, A. (2018). Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF; ISBN 978-989-758-281-3; ISSN 2184-4305, SciTePress, pages 478-485. DOI: 10.5220/0006598904780485

@conference{healthinf18,
author={Christian Lins. and Sebastian M. Müller. and Max Pfingsthorn. and Marco Eichelberg. and Alexander Gerka. and Andreas Hein.},
title={Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF},
year={2018},
pages={478-485},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006598904780485},
isbn={978-989-758-281-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF
TI - Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation
SN - 978-989-758-281-3
IS - 2184-4305
AU - Lins, C.
AU - Müller, S.
AU - Pfingsthorn, M.
AU - Eichelberg, M.
AU - Gerka, A.
AU - Hein, A.
PY - 2018
SP - 478
EP - 485
DO - 10.5220/0006598904780485
PB - SciTePress