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Paper Abstract and Keywords
Presentation 2025-07-24 10:25
A Study on Training Data Reduction for Machine Learning-Based In-Body Localization
Taiki Egusa, Takumi Kobayashi (Osaka Metropolitan Univ.), Manato Fujimoto (Osaka Metropolitan Univ./RIKEN), Shingo Ata, Daisuke Anzai (Osaka Metropolitan Univ.) MICT2025-19
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, research on cybernetic avatars that operate within the human body in place of the individual has been actively pursued, aiming to contribute to the realization of a society characterized by healthy longevity. These avatars are implemented through various implantable devices, and accurate localization of such devices is essential for precisely understanding the internal bodily environment. In our previous study, we achieved high localization accuracy using a machine learning-based method that leverages magnetic fields. However, a major challenge remained—the substantial amount of time required to collect training data. In the present study, we investigate how varying the amount of training data affects the inference accuracy of the proposed method. We further analyze the minimum volume of training data necessary to achieve a target level of accuracy. As a result, we found that the amount of training data could be reduced by 75% while maintaining comparable accuracy in terms of Root Mean Square Error (RMSE), and by 50% based on Error Rate evaluations.
Keyword (in Japanese) (See Japanese page) 
(in English) Cybernetic Avatar / Implantable Device Localization / Machine Learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 129, MICT2025-19, pp. 7-12, July 2025.
Paper # MICT2025-19 
Date of Issue 2025-07-17 (MICT) 
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 MICT2025-19

Conference Information
Committee SRW MICT  
Conference Date 2025-07-24 - 2025-07-24 
Place (in Japanese) (See Japanese page) 
Place (in English) YAMAGATA TERRSA 
Topics (in Japanese) (See Japanese page) 
Topics (in English) MICT/SRW Joint Workshop 
Paper Information
Registration To MICT 
Conference Code 2025-07-SRW-MICT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Training Data Reduction for Machine Learning-Based In-Body Localization 
Sub Title (in English)  
Keyword(1) Cybernetic Avatar  
Keyword(2) Implantable Device Localization  
Keyword(3) Machine Learning  
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1st Author's Name Taiki Egusa  
1st Author's Affiliation Osaka Metropolitan University (Osaka Metropolitan Univ.)
2nd Author's Name Takumi Kobayashi  
2nd Author's Affiliation Osaka Metropolitan University (Osaka Metropolitan Univ.)
3rd Author's Name Manato Fujimoto  
3rd Author's Affiliation Osaka Metropolitan University/RIKEN Center for Advanced Intelligence Project (Osaka Metropolitan Univ./RIKEN)
4th Author's Name Shingo Ata  
4th Author's Affiliation Osaka Metropolitan University (Osaka Metropolitan Univ.)
5th Author's Name Daisuke Anzai  
5th Author's Affiliation Osaka Metropolitan University (Osaka Metropolitan Univ.)
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Speaker Author-1 
Date Time 2025-07-24 10:25:00 
Presentation Time 25 minutes 
Registration for MICT 
Paper # MICT2025-19 
Volume (vol) vol.125 
Number (no) no.129 
Page pp.7-12 
#Pages 6 
Date of Issue 2025-07-17 (MICT) 

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