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| Presentation | 2020-01-10 10:00 Prediction method of tumble using machine learning of footsteps. -- Evaluation results that increased the number of subjects. -- Takehiro Mori, Hiroyuki Nishi, Manabu Okamoto (Sojo Univ.)  ICM2019-36 LOIS2019-51 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | With the progress of super-aged society, the number of elderlies living alone has increased, and the number of falls in the home has also increased. In order to improve these situations, we are studying a walking sound identification method that predicts walking movements using elderly walking sounds and prevents falls. In this study, we focus on the fact that elderly people tend to walk on sliding feet as a pre-step to fall. The fact that the acoustic features of walking sound are greatly different between normal walking and sliding feet is used. After the two are identified using a neural network, if sliding feet is detected, the elderlies are warned or alerted to prevent falls. As an evaluation of the discrimination method, we recorded normal walking sounds from barefoot, slippers, and socks, and walking sounds from the normal and sliding footsteps. The walking sound of multiple people was used. We examined the effects of the learning method on the discrimination performance, such as changes in performance depending on whether footwear and walking people are distinguished as learning categories, and performance when evaluating walking people that were not used for learning. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Elderly people / Observation / Neural network / tumble / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 359, LOIS2019-51, pp. 33-37, Jan. 2020. | |
| Paper # | LOIS2019-51 | |
| Date of Issue | 2020-01-02 (ICM, LOIS) | |
| 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 | ICM2019-36 LOIS2019-51 |
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| Conference Information | |
| Committee | LOIS ICM |
| Conference Date | 2020-01-09 - 2020-01-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | ARKAS SASEBO |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Practical Use of Lifelog, Office Information System, Business Management, etc. |
| Paper Information | |
| Registration To | LOIS |
| Conference Code | 2020-01-LOIS-ICM |
| 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) | Evaluation results that increased the number of subjects. |
| Keyword(1) | Elderly people |
| Keyword(2) | Observation |
| Keyword(3) | Neural network |
| Keyword(4) | tumble |
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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 | Manabu Okamoto |
| 3rd Author's Affiliation | Sojo University (Sojo Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-01-10 10:00:00 |
| Presentation Time | 25 minutes |
| Registration for | LOIS |
| Paper # | ICM2019-36, LOIS2019-51 |
| Volume (vol) | vol.119 |
| Number (no) | no.358(ICM), no.359(LOIS) |
| Page | pp.33-37 |
| #Pages | 5 |
| Date of Issue | 2020-01-02 (ICM, LOIS) |
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