Repetitive motion analysis: segmentation and event classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
ChunMei Lu, Nicola J Ferrier

Abstract

Acquisition, analysis, and classification of repetitive human motion for the assessment of postural stress is of central importance to ergonomics practitioners. We present a two-threshold, multidimensional segmentation algorithm to automatically decompose a complex motion into a sequence of simple linear dynamic models. No a priori assumptions were made about the number of models that comprise the full motion or about the duration of the task cycle. A compact motion representation is obtained for each segment using parameters of a damped harmonic dynamic model. Event classification was performed using cluster analysis with the model parameters as input. Experiments demonstrate the technique on complex motion.

References

Sep 1, 1995·IEEE Transactions on Bio-medical Engineering·T Y Yen, R G Radwin
Jun 1, 1995·Biological cybernetics·Y Wada, M Kawato
Apr 1, 1997·American Industrial Hygiene Association Journal·W A LatkoS S Ulin
Mar 6, 2004·IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society·ChunMei Lu, Nicola J Ferrier

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Citations

Feb 10, 2009·Journal of Biomechanics·Newman LauDaniel Chow
Jun 27, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Feng ZhouJessica K Hodgins
Dec 22, 2010·IEEE Transactions on Visualization and Computer Graphics· Cheng Chen Jun Xiao
May 15, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Alexia Briassouli, Narendra Ahuja
Oct 16, 2004·IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society·ChunMei Lu, Nicola J Ferrier

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