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

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.

• 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
• Contract Date: Apr 10, 2018