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Paper Abstract and Keywords
Presentation 2023-01-19 15:15
[Short Paper] A Study on Improving the Robustness of Wi-Fi Sensing against Environmental Change
Sorachi Kato (OU), Tomoki Murakami (NTT), Takuya Fujihashi, Takashi Watanabe, Shunsuke Saruwatari (OU) SeMI2022-82
Abstract (in Japanese) (See Japanese page) 
(in English) There is research on invasive sensing of objects using Channel State Information (CSI), which represents the propagation state of Wi-Fi radio waves.
Neural networks are used to analyze CSI fluctuations, and have been shown to achieve high sensing accuracy in the fields of location estimation and activity recognition.
On the other hand, there is a problem that sensing accuracy is significantly degraded when a new CSI acquired at a different time and environment from the one used for training.
Re-training or transfer learning is necessary to adapt the model to the new environment, but in some practical applications, only a small amount of data from the new environment can be obtained, or sometimes none at all, due to the highly specialized methods used to collect label data for training.
In this paper, we propose a method for respiratory rate estimation that transforms CSI features in different environments into a single domain, thereby guaranteeing sensing accuracy without label data in those environments.
The method has a domain adaptation module that transforms CSI features acquired in different environments into a single domain based on the idea of style transformation in the image processing field.
The method also uses Template of CSI and respiration sensor data, which are pre-obtained to include information on respiration at various frequencies.
By adapting Template CSI to the characteristics of CSI in different environments and combining it with Template respiration sensor data to train a convolutional neural network (CNN), it is possible to train models that adapt to different environments using only unlabeled CSI in different environments.
This paper shows that the mean absolute percentage error of the proposed method is reduced by about 20% compared to the case without domain adaptation.
Keyword (in Japanese) (See Japanese page) 
(in English) IEEE 802.11 / Wireless Sensing / Domain Adaptation / Vital Sensing / Wi-Fi / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 341, SeMI2022-82, pp. 49-50, Jan. 2023.
Paper # SeMI2022-82 
Date of Issue 2023-01-12 (SeMI) 
ISSN Online edition: ISSN 2432-6380
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 SeMI  
Conference Date 2023-01-19 - 2023-01-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Naruto grand hotel 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SeMI 
Conference Code 2023-01-SeMI-SeMI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Improving the Robustness of Wi-Fi Sensing against Environmental Change 
Sub Title (in English)  
Keyword(1) IEEE 802.11  
Keyword(2) Wireless Sensing  
Keyword(3) Domain Adaptation  
Keyword(4) Vital Sensing  
Keyword(5) Wi-Fi  
1st Author's Name Sorachi Kato  
1st Author's Affiliation Osaka University (OU)
2nd Author's Name Tomoki Murakami  
2nd Author's Affiliation Access Network Service Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Takuya Fujihashi  
3rd Author's Affiliation Osaka University (OU)
4th Author's Name Takashi Watanabe  
4th Author's Affiliation Osaka University (OU)
5th Author's Name Shunsuke Saruwatari  
5th Author's Affiliation Osaka University (OU)
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Speaker Author-1 
Date Time 2023-01-19 15:15:00 
Presentation Time 10 minutes 
Registration for SeMI 
Paper # SeMI2022-82 
Volume (vol) vol.122 
Number (no) no.341 
Page pp.49-50 
Date of Issue 2023-01-12 (SeMI) 

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