Published January 1, 2014
| Version v1
Conference paper
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Online quality assessment of human movement from skeleton data
Authors/Creators
Description
We propose a general method for online estimation of the quality of movement 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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