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| Presentation | 2021-03-01 10:25 Fall Detection Based on LSTM Using Accelerometer Yoshiya Uotani, Chen Ye, Kohei Yamamoto, Tomoaki Ohtsuki (Keio)  SeMI2020-59 |
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| 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 |
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| 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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