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| Paper Abstract and Keywords | ||
| Presentation | 2022-09-16 11:15 A Study on Body Water Content Estimation Using Machine Learning Based on Relative Permittivity Measurement Takuto Asai, Daisuke Anzai, Jainqing Wang (NITech)  CQ2022-41 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, the number of casualties due to dehydration in hot and humid environments has been increasing. In addition, dialysis patients are unable to eject their body water by themselves due to decreased renal functions. Therefore, appropriate prevention and treatment is important by accuracy estimating the body water content in real time. So this study focused on an estimation method of the body water content based on measured dielectric constants using a wearable device. In order to apply the estimation method to heterogeneous medium of the human body, this paper examines the effectiveness of water content estimation by machine learning. From preliminary experimental evaluation results, the water content can be estimated with a relative error of less than 0.1 %. And furthermore, the water content outside of the range of the training data can be estimated with a relative error of less than 0.4 %. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Complex relative permittivity / Lichtenecker’s exponential law / Machine learning / Water content estimation / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 184, CQ2022-41, pp. 97-100, Sept. 2022. | |
| Paper # | CQ2022-41 | |
| Date of Issue | 2022-09-08 (CQ) | |
| 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 | CQ2022-41 |
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| Conference Information | |
| Committee | IA CQ MIKA |
| Conference Date | 2022-09-15 - 2022-09-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido Citizens Actives Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Wireless Communications Quality, 6G, IoT, Cross layer Technologies, Internet Operation and Management, etc. |
| Paper Information | |
| Registration To | CQ |
| Conference Code | 2022-09-IA-CQ-MIKA |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Body Water Content Estimation Using Machine Learning Based on Relative Permittivity Measurement |
| Sub Title (in English) | |
| Keyword(1) | Complex relative permittivity |
| Keyword(2) | Lichtenecker’s exponential law |
| Keyword(3) | Machine learning |
| Keyword(4) | Water content estimation |
| Keyword(5) | |
| Keyword(6) | |
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| 1st Author's Name | Takuto Asai |
| 1st Author's Affiliation | Nagoya Institute of Technology (NITech) |
| 2nd Author's Name | Daisuke Anzai |
| 2nd Author's Affiliation | Nagoya Institute of Technology (NITech) |
| 3rd Author's Name | Jainqing Wang |
| 3rd Author's Affiliation | Nagoya Institute of Technology (NITech) |
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| Speaker | Author-1 |
| Date Time | 2022-09-16 11:15:00 |
| Presentation Time | 25 minutes |
| Registration for | CQ |
| Paper # | CQ2022-41 |
| Volume (vol) | vol.122 |
| Number (no) | no.184 |
| Page | pp.97-100 |
| #Pages | 4 |
| Date of Issue | 2022-09-08 (CQ) |
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