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Paper Abstract and Keywords
Presentation 2017-11-10 10:00
Prediction method of tumble using machine learning of footsteps.
Takehiro Mori, Hiroyuki Nishi, Yoshimasa Kimura (Sojo Univ.) ISEC2017-61 SITE2017-43 LOIS2017-38
Abstract (in Japanese) (See Japanese page) 
(in English) Along with the progress of super aged society, lonely elderly people are increasing. In addition, accidents in homes and out of the home of elderly people also increased, and accidents caused by falls accounted for about 22% of in-home accidents. In order to improve this, the authors have predicted a fall using the walking sound of elderly people and are investigating a walking sound identification method to prevent falls beforehand.
In this research, we focus on the fact that elderly people tend to walk on the shank as a preliminary stage of falling. By using the fact that the acoustic features of walking sounds are different greatly between normal walking and sleeping, by using a neural network to distinguish them, when a surrogate is detected, a warning or a caution alert is given to the elderly It prevents falls by giving.
On the premise of a flooring floor, when wearing slippers, we recorded ordinary walking sounds by bare feet and walking sounds of abrasion for about 5 minutes, and a discrimination experiment was conducted with half of them as learning and the rest as evaluation.
As a result of the evaluation experiment using the dimension number of the intermediate layer as a parameter, 95.5% of the highest performance was obtained when the number of dimensions is 20. As a result, we were able to detect the survival state with high accuracy, give a warning to the elderly, and obtain a prospect of preventing falls.
Keyword (in Japanese) (See Japanese page) 
(in English) Elderly people / Observation / footsteps / Neural network / tumble / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 287, LOIS2017-38, pp. 79-82, Nov. 2017.
Paper # LOIS2017-38 
Date of Issue 2017-11-02 (ISEC, SITE, LOIS) 
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 ISEC2017-61 SITE2017-43 LOIS2017-38

Conference Information
Committee LOIS ISEC SITE  
Conference Date 2017-11-09 - 2017-11-10 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To LOIS 
Conference Code 2017-11-LOIS-ISEC-SITE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Prediction method of tumble using machine learning of footsteps. 
Sub Title (in English)  
Keyword(1) Elderly people  
Keyword(2) Observation  
Keyword(3) footsteps  
Keyword(4) Neural network  
Keyword(5) tumble  
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Keyword(7)  
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1st Author's Name Takehiro Mori  
1st Author's Affiliation Sojo University (Sojo Univ.)
2nd Author's Name Hiroyuki Nishi  
2nd Author's Affiliation Sojo University (Sojo Univ.)
3rd Author's Name Yoshimasa Kimura  
3rd Author's Affiliation Sojo University (Sojo Univ.)
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Speaker Author-1 
Date Time 2017-11-10 10:00:00 
Presentation Time 30 minutes 
Registration for LOIS 
Paper # ISEC2017-61, SITE2017-43, LOIS2017-38 
Volume (vol) vol.117 
Number (no) no.285(ISEC), no.286(SITE), no.287(LOIS) 
Page pp.79-82 
#Pages 4 
Date of Issue 2017-11-02 (ISEC, SITE, LOIS) 

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