Human gait modeling and statistical registration for the frontal view gait data with application to the normal/abnormal gait analysis

Kosuke Okusa, Toshinari Kamakura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationIAENG Transactions on Engineering Technologies - Special Issue of the World Congress on Engineering and Computer Science 2012
PublisherSpringer Verlag
Pages525-539
Number of pages15
ISBN (Print)9789400768178
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventWorld Congress on Engineering and Computer Science, WCECS 2012 - San Francisco, CA, United States
Duration: Oct 24 2012Oct 26 2012

Publication series

NameLecture Notes in Electrical Engineering
Volume247 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Other

OtherWorld Congress on Engineering and Computer Science, WCECS 2012
Country/TerritoryUnited States
CitySan Francisco, CA
Period10/24/1210/26/12

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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