Gesture recognition for interactive exercise programs.

Jedediah Perkins, Misha Pavel, Holly B. Jimison, Susan Scott

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1915-1917
Number of pages3
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
StatePublished - 2008
Externally publishedYes

Fingerprint

Gesture recognition
Gestures
Hidden Markov models
Classifiers
Head

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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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.",
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