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Human Gait Modeling and Statistical Registration for the Frontal View Gait Data with Application to the Normal/Abnormal Gait Analysis

  • Kosuke Okusa
  • Toshinari Kamakura
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 247)

Abstract

We study the problem of analyzing and classifying frontal view human gait data by registration and modeling on a video data. In this study, we suppose that frontal view gait data as a mixing of scale changing, human movements and speed changing parameter. 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 detecting. In abnormal gait detecting experiment, we apply K-NN 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.

Keywords

Abnormal gait detection Frontal view gait data Gait analysis Human gait modeling K-nearest neighbor classifier parameter estimation Scale registration speed estimation statistical registration  

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Science and EngineeringChuo UniversityTokyoJapan

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