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Paper Abstract and Keywords
Presentation 2012-03-10 13:50
Motion Classification Method using Probabilistic Graphical Model and Its Application to Working Posture Analysis
Masaru Okamoto, Yuko Akai, Yukihiro Matsubara (Hiroshima City Univ.) ET2011-120
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, novel probabilistic motion classification method using position data of body measured from various cameras. In order to classify user’s motion without influence of missing value of measured data, Bayesian network, which is a kind of probabilistic graphical model, is utilized as classifier. Moreover, posterior probabilities, estimated Bayesian networks using different body information measured from multiple cameras, are combined based on entropy of estimated posterior probabilities to classify measured data into motions. Experiments of working posture classification have been conducted to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method improved the robustness and stability of motion classification.
Keyword (in Japanese) (See Japanese page) 
(in English) posture analysis / Bayesian network / skill learning / posterior probability / / / /  
Reference Info. IEICE Tech. Rep., vol. 111, no. 473, ET2011-120, pp. 113-118, March 2012.
Paper # ET2011-120 
Date of Issue 2012-03-03 (ET) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF ET2011-120

Conference Information
Committee ET  
Conference Date 2012-03-10 - 2012-03-10 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To ET 
Conference Code 2012-03-ET 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Motion Classification Method using Probabilistic Graphical Model and Its Application to Working Posture Analysis 
Sub Title (in English)  
Keyword(1) posture analysis  
Keyword(2) Bayesian network  
Keyword(3) skill learning  
Keyword(4) posterior probability  
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1st Author's Name Masaru Okamoto  
1st Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
2nd Author's Name Yuko Akai  
2nd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
3rd Author's Name Yukihiro Matsubara  
3rd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
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Speaker Author-1 
Date Time 2012-03-10 13:50:00 
Presentation Time 20 minutes 
Registration for ET 
Paper # ET2011-120 
Volume (vol) vol.111 
Number (no) no.473 
Page pp.113-118 
#Pages 6 
Date of Issue 2012-03-03 (ET) 

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