Segmenting human motion for automated rehabilitation exercise analysis.
This paper proposes an approach for the automated segmentation and identification of movement segments from continuous time series data of human movement, collected through motion capture of ambulatory sensors. The proposed approach uses a two stage identification and recognition process, based on velocity and stochastic modeling of each motion to be identified. In the first stage, motion segment candidates are identified based on a unique sequence of velocity features such as velocity peaks and zero velocity crossings. In the second stage, Hidden Markov models are used to accurately identify segment locations from the identified candidates. The approach is capable of on-line segmentation and identification, enabling interactive feedback in rehabilitation applications. The approach is validated on a rehabilitation movement dataset, and achieves a segmentation accuracy of 89%.
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada. jf2lin@uwaterloo.ca
Jonathan
J
Feng-Shun Lin
Dana
D
Kulić
D000465:Algorithms; D005081:Exercise Therapy; D006801:Humans; D008390:Markov Chains; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated; D012046:Rehabilitation