title: Human motion segmentation by data point classification.
abstract: Contemporary physiotherapy and rehabilitation practice uses subjective measures for motion evaluation and requires time-consuming supervision. Algorithms that can accurately segment patient movement would provide valuable data for progress tracking and on-line patient feedback. In this paper, we propose a two-class classifier approach to label each data point in the patient movement data as either a segment point or a non-segment point. The proposed technique was applied to 20 healthy subjects performing lower body rehabilitation exercises, and achieves a segmentation accuracy of 82%.
authors.forename: Jonathan Feng-Shun
authors.initials: JF
authors.lastname: Lin
authors.forename: Vladimir
authors.initials: V
authors.lastname: Joukov
authors.forename: Dana
authors.initials: D
authors.lastname: Kulic
mesh_terms: D000465:Algorithms; D006801:Humans; D007719:Knee Joint / Q000502:physiology; D009068:Movement; D012046:Rehabilitation; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine