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| Presentation | 2022-07-15 15:05 A Predictive Model of Heat Stress Using Heart Rate Variability Analysis Yusuke Shimada, Masashi Sugano (Osaka Metro. Univ.)  SeMI2022-47 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Predicting and controlling heat stress leads to comfort. Because People have different feelings against heat, we need a method to create tuned predictive models for each person to make a comfortable environment. Prior research exists on creating a model suitable for everyone. However, we had to get feedbacks from users such as hot or cold to collect the label data. The objective of the study was to create a model of comfort without direct interaction with the user, and we examined the use of heart rate variability analysis to create a model. When the heart beats and generates an electrical signal, an electrical signal, and we can measure the sharpest peak of this signal called the R wave. We call the difference between the onset time of the R wave and the next R wave RRI, and by sampling the RRI for several minutes and performing spectral analysis, the RRI can be broken down by frequency. We can extract two components from that called LF and HF. Since LF represents sympathetic nervous system activity and HF represents parasympathetic nervous system activity, this ratio can be used to express stress, which is called LF/HF. In the data measurement procedure, the heart rate sensor is first connected to the Raspberry Pi via Bluetooth communication, and it send the LF/HF obtained after 3 minutes of measurement to the Raspberry Pi. Then, another sensor also sends the temperature and humidity to Raspberry Pi via Bluetooth communication, and we combined LF/HF and these two parameters into one data set. We conducted the experiment on ten university students. We employed Ridge regression as the prediction model and introduced transfer learning to compensate for the lack of data. In addition, we calculated RMSE using one-out cross-validation to evaluate the performance of the model. It was shown that the accuracy of the prediction model for stress indicators can be improved by using transition learning. On the other hand, we found various issues such as the types of features, the construction of an accurate experimental environment, and the difficulty of data acquisition. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Heart rate variability / Heat stress / Transfer learning / Ridge regression / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 108, SeMI2022-47, pp. 127-132, July 2022. | |
| Paper # | SeMI2022-47 | |
| Date of Issue | 2022-07-06 (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 | SeMI2022-47 |
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| Conference Information | |
| Committee | NS SR RCS SeMI RCC |
| Conference Date | 2022-07-13 - 2022-07-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | The Kanazawa Theatre + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Distributed Wireless Network, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2022-07-NS-SR-RCS-SeMI-RCC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Predictive Model of Heat Stress Using Heart Rate Variability Analysis |
| Sub Title (in English) | |
| Keyword(1) | Heart rate variability |
| Keyword(2) | Heat stress |
| Keyword(3) | Transfer learning |
| Keyword(4) | Ridge regression |
| Keyword(5) | |
| Keyword(6) | |
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| 1st Author's Name | Yusuke Shimada |
| 1st Author's Affiliation | Osaka Metropolitan University (Osaka Metro. Univ.) |
| 2nd Author's Name | Masashi Sugano |
| 2nd Author's Affiliation | Osaka Metropolitan University (Osaka Metro. Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-07-15 15:05:00 |
| Presentation Time | 25 minutes |
| Registration for | SeMI |
| Paper # | SeMI2022-47 |
| Volume (vol) | vol.122 |
| Number (no) | no.108 |
| Page | pp.127-132 |
| #Pages | 6 |
| Date of Issue | 2022-07-06 (SeMI) |
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