Projects per year
Abstract
We propose a general method for online estimation of the quality of movements from Kinect skeleton data. A robust non-linear manifold learning technique is used to reduce the dimensionality of the noisy skeleton data. Then, a statistical model of normal movement is built from observations of healthy subjects, and the level of matching of new observations with this model is computed on a frame-by-frame basis following Markovian assumptions. The proposed method is validated on the assessment of gait on stairs.
Original language | English |
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Publication status | Published - Sept 2014 |
Event | British Machine Vision Conference - University of Nottingham, Nottingham, United Kingdom Duration: 1 Sept 2014 → 5 Sept 2014 |
Conference
Conference | British Machine Vision Conference |
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Country/Territory | United Kingdom |
City | Nottingham |
Period | 1/09/14 → 5/09/14 |
Structured keywords
- Digital Health
- SPHERE
Keywords
- Digital Health
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Dive into the research topics of 'Online quality assessment of human movement from skeleton data'. 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