Online quality assessment of human motion from skeleton data
In Proceedings British Machine Vision Conference 2014
AbstractWe 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.
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