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Paper Abstract and Keywords
Presentation 2019-11-05 10:00
A study of machine learning algorithm for wearable biosignal sensor
Daisuke Watanabe, Yuji Yano, Shintaro Izumi, Hiroshi Kawaguchi, Masahiko Yosimoto (Kobe Univ.) MICT2019-25 MI2019-52
Abstract (in Japanese) (See Japanese page) 
(in English) The algorithm was evaluated assuming that edge inference was performed on the data obtained from the wearable biological information sensor. For three applications for wearable healthcare, we evaluated the algorithm from the viewpoint of inference accuracy and energy efficiency by implementing a random forest (RF) and convolutional neural network (CNN) with FPGA. As a result, RF increases energy efficiency by one to three orders of magnitude, making it suitable for low power applications. On the other hand, inferior accuracy, CNN is 3% to 10% high, so it is suitable for applications that require high accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) IoT / wearable healthcare / machine learning / low power inference / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 263, MICT2019-25, pp. 7-8, Nov. 2019.
Paper # MICT2019-25 
Date of Issue 2019-10-29 (MICT, MI) 
ISSN Print edition: ISSN 0913-5685  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)
Download PDF MICT2019-25 MI2019-52

Conference Information
Committee MI MICT  
Conference Date 2019-11-05 - 2019-11-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Univ. of Tsukuba 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical imaging technology, healthcare and medical information communication technology 
Paper Information
Registration To MICT 
Conference Code 2019-11-MI-MICT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study of machine learning algorithm for wearable biosignal sensor 
Sub Title (in English)  
Keyword(1) IoT  
Keyword(2) wearable healthcare  
Keyword(3) machine learning  
Keyword(4) low power inference  
1st Author's Name Daisuke Watanabe  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Yuji Yano  
2nd Author's Affiliation Kobe University (Kobe Univ.)
3rd Author's Name Shintaro Izumi  
3rd Author's Affiliation Kobe University (Kobe Univ.)
4th Author's Name Hiroshi Kawaguchi  
4th Author's Affiliation Kobe University (Kobe Univ.)
5th Author's Name Masahiko Yosimoto  
5th Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2019-11-05 10:00:00 
Presentation Time 20 minutes 
Registration for MICT 
Paper # MICT2019-25, MI2019-52 
Volume (vol) vol.119 
Number (no) no.263(MICT), no.264(MI) 
Page pp.7-8 
Date of Issue 2019-10-29 (MICT, MI) 

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