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Repetitive motion analysis: segmentation and event classification.

ChunMei Lu·Nicola J Ferrier
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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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AlgorithmsArmArtificial IntelligenceCluster AnalysisHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalLegModels, BiologicalMotionMovementPattern Recognition, AutomatedPeriodicityReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedSubtraction TechniqueComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.Validation Study

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ChunMei Lu, Nicola J Ferrier. 2004. Repetitive motion analysis: segmentation and event classification.. https://doi.org/10.1109/tpami.2004.1262196

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