Online quality assessment of human motion from skeleton data
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.79
Abstract
We propose a general method for online estimation of the quality of movements from Kinect skeleton data. 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. A robust non-linear manifold learning technique is used to reduce the dimensionality of the noisy skeleton data. The proposed method is validated in two different contexts, i.e. the assessment of gait on stairs and analysing cross punches in boxing.
Session
Poster Session
Files
Extended Abstract (PDF, 1 page, 149K)Paper (PDF, 12 pages, 1.2M)
Bibtex File
Citation
Adeline Paiement, Lili Tao, Massimo Camplani, Sion Hannuna, Dima Damen, and Majid Mirmehdi. Online quality assessment of human motion from skeleton data. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.79
title = {Online quality assessment of human motion from skeleton data},
author = {Paiement, Adeline and Tao, Lili and Camplani, Massimo and Hannuna, Sion and Damen, Dima and Mirmehdi, Majid},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.79 }
}