title: Repetitive motion analysis: segmentation and event classification.
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.
authors.affiliation: Center for Mathematical Sciences and Department of Mechanical Engineering, University of Wisconsin, Madison, WI 53706, USA. chunmei@cae.wisc.edu
authors.forename: ChunMei
authors.initials: C
authors.lastname: Lu
authors.forename: Nicola J
authors.initials: NJ
authors.lastname: Ferrier
mesh_terms: D000465:Algorithms; D001132:Arm / Q000033:anatomy & histology / Q000502:physiology; D001185:Artificial Intelligence; D016000:Cluster Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007866:Leg / Q000033:anatomy & histology / Q000502:physiology; D008954:Models, Biological; D009038:Motion; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated; D010507:Periodicity; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique