Gait parameter and speed estimation from the frontal view gait video data based on the gait motion and spatial modeling

Kosuke Okusa, Toshinari Kamakura

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

We study the problem of analyzing and classifying frontal view gait video data. In this study, we suppose that frontal view gait data as a mixing of scale changing, human movements and speed changing parameters. We estimate these parameters using the statistical registration and modeling on a video data. Our gait model is based on human gait structure and temporal-spatial relations between camera and subject. To demonstrate the effectiveness of our method, we conducted two sets of experiments, assessing the proposed method in gait analysis for young/elderly person and abnormal gait detection. In abnormal gait detection experiment, we apply K-nearestneighbor classifier, using the estimated parameters, to perform normal/abnormal gait detect, and present results from an experiment involving 120 subjects (young person), and 60 subjects (elderly person). As a result, our method shows high detection rate.

Original languageEnglish
Pages (from-to)37-44
Number of pages8
JournalIAENG International Journal of Applied Mathematics
Volume43
Issue number1
Publication statusPublished - Feb 1 2013
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Applied Mathematics

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