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Repetitive Motion Analysis: Segmentation and Event Classification
February 2004 (vol. 26 no. 2)
pp. 258-263
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.
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Index Terms:
Motion analysis, motion classification, event detection.
Citation:
ChunMei Lu, Nicola J. Ferrier, "Repetitive Motion Analysis: Segmentation and Event Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 258-263, Jan. 2004, doi:10.1109/TPAMI.2004.1262196
