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Online quality assessment of human movement from skeleton data

Adeline Paiement, Lili Tao, Sion Hannuna, Massimo Camplani, Dima Damen, Majid Mirmehdi, Online quality assessment of human movement from skeleton data. Proceedings of the British Machine Vision Conference 2014. September 2014. PDF, 1200 Kbytes.

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.

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