Projects per year
Abstract
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.
Original language | English |
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Pages (from-to) | 136-152 |
Number of pages | 17 |
Journal | Computer Vision and Image Understanding |
Volume | 148 |
Early online date | 27 May 2016 |
DOIs | |
Publication status | Published - 1 Jul 2016 |
Structured keywords
- Digital Health
- SPHERE
Keywords
- Human Motion Quality
- Human Motion Assessment
- Continuous-State HMM Motion Analysis
- Motion Abnormality Detection
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Dive into the research topics of 'A comparative study of pose representation and dynamics modelling for online motion quality assessment'. Together they form a unique fingerprint.Projects
- 1 Finished
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SPHERE (EPSRC IRC)
Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Damen, D., Gooberman-Hill, R., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.
1/10/13 → 30/09/18
Project: Research, Parent
Profiles
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Professor Dima Damen
- School of Computer Science - Professor in Computer Vision
- Bristol Vision Institute
- Visual Information Laboratory
Person: Academic , Member