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A comparative study of pose representation and dynamics modelling for online motion quality assessment

Tao, Lili; Paiement, Adeline; Damen, Dima; Mirmehdi, Majid; Hannuna, Sion; Camplani, Massimo; Burghardt, Tilo; Craddock, Ian

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Authors

Lili Tao

Adeline Paiement

Dima Damen

Majid Mirmehdi

Sion Hannuna

Massimo Camplani

Tilo Burghardt

Ian Craddock



Abstract

© 2015 The Authors. Published by Elsevier Inc. Quantitative assessment of the quality of motion is increasingly in demand by clinicians in healthcare and rehabilitation monitoring of patients. We study and compare the performances of different pose representations and HMM models of dynamics of movement for online quality assessment of human motion. In a general sense, our assessment framework builds a model of normal human motion from skeleton-based samples of healthy individuals. It encapsulates the dynamics of human body pose using robust manifold representation and a first-order Markovian assumption. We then assess deviations from it via a continuous online measure. We compare different feature representations, reduced dimensionality spaces, and HMM models on motions typically tested in clinical settings, such as gait on stairs and flat surfaces, and transitions between sitting and standing. Our dataset is manually labelled by a qualified physiotherapist. The continuous-state HMM, combined with pose representation based on body-joints' location, outperforms standard discrete-state HMM approaches and other skeleton-based features in detecting gait abnormalities, as well as assessing deviations from the motion model on a frame-by-frame basis.

Citation

Tao, L., Paiement, A., Damen, D., Mirmehdi, M., Hannuna, S., Camplani, M., …Craddock, I. (2016). A comparative study of pose representation and dynamics modelling for online motion quality assessment. Computer Vision and Image Understanding, 148, 136-152. https://doi.org/10.1016/j.cviu.2015.11.016

Journal Article Type Article
Acceptance Date Nov 28, 2015
Online Publication Date May 27, 2016
Publication Date Jul 1, 2016
Deposit Date Apr 10, 2018
Publicly Available Date Apr 10, 2018
Journal Computer Vision and Image Understanding
Print ISSN 1077-3142
Electronic ISSN 1090-235X
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 148
Pages 136-152
DOI https://doi.org/10.1016/j.cviu.2015.11.016
Public URL https://uwe-repository.worktribe.com/output/909402
Publisher URL https://doi.org/10.1016/j.cviu.2015.11.016

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