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Authors: Julia Richter ; Christian Wiede ; Bharat Shinde and Gangolf Hirtz

Affiliation: Technische Universität Chemnitz, Germany

ISBN: 978-989-758-222-6

Keyword(s): Health Care, Medical Training Therapy, Support Vector Machines, Motion Sequence Matching, Incremental Dynamic Time Warping.

Related Ontology Subjects/Areas/Topics: Applications ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Classification ; Computer Vision, Visualization and Computer Graphics ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Imaging ; Methodologies and Methods ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Multimedia ; Multimedia Signal Processing ; Pattern Recognition ; Physiological Computing Systems ; Signal Processing ; Software Engineering ; Telecommunications ; Theory and Methods

Abstract: Preventive and therapeutic measures can contribute to maintain or to regain physical abilities. In Germany, the growing number of elderly people is posing serious challenges for the therapeutic sector. Therefore, the objective that has been pursued in recent research is to assist patients during their medical training by reproducing therapists' feedback. Extant systems have been limited to feedback that is based on the evaluation of only the similarity between a pre-recorded reference and the currently performed motion. To date, very little is known about feedback generation that exceeds such similarity evaluations. Moreover, current systems require a personalised, pre-recorded reference for each patient in order to compare the reference against the motion performed during the exercise and to generate feedback. The aim of this study is to develop and evaluate an error classification algorithm for therapy exercises using Incremental Dynamic Time Warping and 3-D skeleton joint informati on. Furthermore, a normalisation method that allows the utilisation of non-personalised references has been investigated. In our experiments, we were able to successfully identify errors, even for non-personalised reference data, by using normalised hierarchical coordinates. (More)

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Paper citation in several formats:
Richter J., Wiede C., Shinde B. and Hirtz G. (2017). Motion Error Classification for Assisted Physical Therapy - A Novel Approach using Incremental Dynamic Time Warping and Normalised Hierarchical Skeleton Joint Data.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 281-288. DOI: 10.5220/0006108002810288

@conference{icpram17,
author={Julia Richter and Christian Wiede and Bharat Shinde and Gangolf Hirtz},
title={Motion Error Classification for Assisted Physical Therapy - A Novel Approach using Incremental Dynamic Time Warping and Normalised Hierarchical Skeleton Joint Data},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={281-288},
publisher={ScitePress},
organization={INSTICC},
doi={10.5220/0006108002810288},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Motion Error Classification for Assisted Physical Therapy - A Novel Approach using Incremental Dynamic Time Warping and Normalised Hierarchical Skeleton Joint Data
SN - 978-989-758-222-6
AU - Richter J.
AU - Wiede C.
AU - Shinde B.
AU - Hirtz G.
PY - 2017
SP - 281
EP - 288
DO - 10.5220/0006108002810288

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