Gesture recognition for interactive exercise programs.
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.
Perkins
Jedediah
J
Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon 97239, USA. jperkins@bme.ogi.edu
Pavel
Misha
M
Jimison
Holly B
HB
Scott
Susan
S
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