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| Presentation | 2020-11-26 17:20 [Poster Presentation] Accuracy Evaluations of LSTM-based RRI Estimation Method by Using Smartphone Sensors During Exercise Satomi Shirasaki, Kenji Kanai, Jiro Katto (Waseda Univ.)  SeMI2020-29 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, because of aging of the population and increasing medical spending, it is required to shift from the conventional treatment centered medical care to preventive treatment. Therefore, demands for early diagnosis and handy monitoring in daily life is increasing. To address this fact, Internet of Things (IoT) and deep learning get more attention. In this paper, we propose an R-R Interval (RRI) estimation method based on deep learning using smartphone sensors to estimate the RRI without using special medical devices. To construct dataset, we collect ECG, 3-axis acceleration, pressure, illuminance, GPS, and temperature while walking and running by using a smart wear called hitoe and a smartphone. By using the dataset, we adopt a dual stage attention based RNN model to estimate RRI and evaluate the accuracy. The evaluation results conclude that the proposed method can estimate RRI and LF/HF with high accuracy. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | RRI estimation / LF/HF estimation / deep learning / IoT / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 261, SeMI2020-29, pp. 57-58, Nov. 2020. | |
| Paper # | SeMI2020-29 | |
| Date of Issue | 2020-11-19 (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 | SeMI2020-29 |
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| Conference Information | |
| Committee | SRW SeMI CNR |
| Conference Date | 2020-11-26 - 2020-11-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | IoT Workshop |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2020-11-SRW-SeMI-CNR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Accuracy Evaluations of LSTM-based RRI Estimation Method by Using Smartphone Sensors During Exercise |
| Sub Title (in English) | |
| Keyword(1) | RRI estimation |
| Keyword(2) | LF/HF estimation |
| Keyword(3) | deep learning |
| Keyword(4) | IoT |
| Keyword(5) | |
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| Keyword(8) | |
| 1st Author's Name | Satomi Shirasaki |
| 1st Author's Affiliation | Waseda University (Waseda Univ.) |
| 2nd Author's Name | Kenji Kanai |
| 2nd Author's Affiliation | Waseda University (Waseda Univ.) |
| 3rd Author's Name | Jiro Katto |
| 3rd Author's Affiliation | Waseda University (Waseda Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-11-26 17:20:00 |
| Presentation Time | 70 minutes |
| Registration for | SeMI |
| Paper # | SeMI2020-29 |
| Volume (vol) | vol.120 |
| Number (no) | no.261 |
| Page | pp.57-58 |
| #Pages | 2 |
| Date of Issue | 2020-11-19 (SeMI) |
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