title: Gesture recognition for interactive exercise programs.
abstract: This paper describes a gesture recognition system which can recognize seated exercises that will be incorporated into an in-home automated interactive exercise program. Hidden Markov Models (HMMs) are used as a motion classifier, with motion features extracted from the grayscale images and the location of the subject's head estimated at initialization. An overall recognition rate of 94.1% is achieved.
authors.affiliation: Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon 97239, USA. jperkins@bme.ogi.edu
authors.forename: Jedediah
authors.initials: J
authors.lastname: Perkins
authors.forename: Misha
authors.initials: M
authors.lastname: Pavel
authors.forename: Holly B
authors.initials: HB
authors.lastname: Jimison
authors.forename: Susan
authors.initials: S
authors.lastname: Scott
mesh_terms: D001698:Biomedical Engineering; D015444:Exercise; D005081:Exercise Therapy / Q000379:methods / Q000706:statistics & numerical data; D005868:Gestures; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008390:Markov Chains; D009068:Movement