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Paper Abstract and Keywords
Presentation 2021-03-01 10:25
Fall Detection Based on LSTM Using Accelerometer
Yoshiya Uotani, Chen Ye, Kohei Yamamoto, Tomoaki Ohtsuki (Keio) SeMI2020-59
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the increase in fall accidents among the elderly has become a problem with the aging of the population. A fall detection method using an accelerometer has been researched and developed. However, conventional fall detection methods do not support the positions of multiple accelerometers. Besides, there is a problem that the detection accuracy is low because they use a classification algorithm that is not suitable for time series prediction, such as Support Vector Machine (SVM) and Random Forest (RF). In this report, we propose fall detection based on LSTM (Long short-term memory) using an accelerometer. In the proposed method, behaviors are learned at multiple sensor installation positions, and the behaviors are classified into multiple classes by LSTM, and then classified into two classes, fall and non-fall. Experiments show that the proposed method achieves higher fall detection accuracy than the conventional method.
Keyword (in Japanese) (See Japanese page) 
(in English) Long short-term memory (LSTM) / 3-axis accelerometer / fall detection / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 382, SeMI2020-59, pp. 7-12, March 2021.
Paper # SeMI2020-59 
Date of Issue 2021-02-22 (SeMI) 
ISSN 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 SeMI2020-59

Conference Information
Committee SeMI IPSJ-MBL IPSJ-UBI  
Conference Date 2021-03-01 - 2021-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Computing, Ubiquitous Computing, etc. 
Paper Information
Registration To SeMI 
Conference Code 2021-03-SeMI-MBL-UBI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fall Detection Based on LSTM Using Accelerometer 
Sub Title (in English)  
Keyword(1) Long short-term memory (LSTM)  
Keyword(2) 3-axis accelerometer  
Keyword(3) fall detection  
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1st Author's Name Yoshiya Uotani  
1st Author's Affiliation Keio University (Keio)
2nd Author's Name Chen Ye  
2nd Author's Affiliation Keio University (Keio)
3rd Author's Name Kohei Yamamoto  
3rd Author's Affiliation Keio University (Keio)
4th Author's Name Tomoaki Ohtsuki  
4th Author's Affiliation Keio University (Keio)
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Speaker Author-1 
Date Time 2021-03-01 10:25:00 
Presentation Time 25 minutes 
Registration for SeMI 
Paper # SeMI2020-59 
Volume (vol) vol.120 
Number (no) no.382 
Page pp.7-12 
#Pages 6 
Date of Issue 2021-02-22 (SeMI) 

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