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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 31, Number 12(2) (2019)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 4061-4068
S&M2069 Research Paper of Special Issue https://doi.org/10.18494/SAM.2019.2452 Published: December 16, 2019 Method of Estimating Heatstroke Risk Using Wristwatch-type Device [PDF] Yoshiyuki Kaiho, Seiichi Takamatsu, and Toshihito Itoh (Received May 31, 2019; Accepted September 10, 2019) Keywords: heatstroke, WBGT, black globe temperature, neural network, wearable device
As a method of estimating the risk of heatstroke with a wearable device, we have developed a method of calculating the wet bulb globe temperature (WBGT) by estimating the black globe temperature (Tg) only from sensors that can be mounted on a wristwatch-type device. In WBGT measurement, the conventional method requires a large sensor for measuring Tg, and it has been difficult to grasp an individual’s heatstroke risk. In this research, we proposed a method of estimating Tg using a neural network and compared the estimation accuracy for different numbers of layers and nodes. In the Tg range of 31 to 41 ℃, it was confirmed that when Tg was estimated by the fully connected neural network of three layers and 20 nodes, the regression coefficient between the measured Tg and the estimated Tg was 0.90, indicating a high accuracy.
Corresponding author: Yoshiyuki Kaiho  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Yoshiyuki Kaiho, Seiichi Takamatsu, and Toshihito Itoh, Method of Estimating Heatstroke Risk Using Wristwatch-type Device, Sens. Mater., Vol. 31, No. 12, 2019, p. 4061-4068. |
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