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Paper Abstract and Keywords
Presentation 2024-03-01 10:30
A Preliminary Study on Parameter Optimization Using a Backpropagation Algorithm for a Neonatal Thermal Model
Natsumi Sakamoto, Hiroki Kudo, Akira Uchiyama (Osaka Univ.), Keisuke Hamada (Nagasaki Harbor Medical Center), Eiji Hirakawa (Kagoshima City Hospital) SeMI2023-81
Abstract (in Japanese) (See Japanese page) 
(in English) Neonates need temperature management in incubators due to their underdeveloped thermoregulatory functions. Traditional methods using skin-attached probes are challenging due to the neonates' delicate skin. Therefore, efforts are being made to realize non-contact temperature measurement using thermography. However, thermography can be hindered during medical procedures by obstructions like medical staff's hands. To address this, we utilize a human thermal model to simulate body temperature changes, integrating real-time sensor data, including thermography and incubator conditions, to estimate surface and core body temperatures. Our method employs backpropagation for rapid parameter optimization in the human thermal model. The effectiveness was confirmed using actual neonatal data, achieving core body temperature estimation with an average absolute error of 0.032 $degree$C, even 10 minutes after thermography data becomes unavailable.
Keyword (in Japanese) (See Japanese page) 
(in English) newborn / core body temperature / human thermal model / backpropagation / Gagge's two-node model / parameter optimization / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 400, SeMI2023-81, pp. 60-65, Feb. 2024.
Paper # SeMI2023-81 
Date of Issue 2024-02-22 (SeMI) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee SeMI IPSJ-UBI IPSJ-MBL  
Conference Date 2024-02-29 - 2024-03-01 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SeMI 
Conference Code 2024-02-SeMI-UBI-MBL 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Preliminary Study on Parameter Optimization Using a Backpropagation Algorithm for a Neonatal Thermal Model 
Sub Title (in English)  
Keyword(1) newborn  
Keyword(2) core body temperature  
Keyword(3) human thermal model  
Keyword(4) backpropagation  
Keyword(5) Gagge's two-node model  
Keyword(6) parameter optimization  
Keyword(7)  
Keyword(8)  
1st Author's Name Natsumi Sakamoto  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Hiroki Kudo  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Akira Uchiyama  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Keisuke Hamada  
4th Author's Affiliation Nagasaki Harbor Medical Center (Nagasaki Harbor Medical Center)
5th Author's Name Eiji Hirakawa  
5th Author's Affiliation Kagoshima City Hospital (Kagoshima City Hospital)
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Speaker Author-1 
Date Time 2024-03-01 10:30:00 
Presentation Time 20 minutes 
Registration for SeMI 
Paper # SeMI2023-81 
Volume (vol) vol.123 
Number (no) no.400 
Page pp.60-65 
#Pages 6 
Date of Issue 2024-02-22 (SeMI) 

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