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Paper Abstract and Keywords
Presentation 2020-10-02 11:00
Future analysis of footstep waveforms and spectrogram using convolutional neural network
Yoshiki Goto, Akitoshi Itai (Chubu Univ.) SIS2020-25
Abstract (in Japanese) (See Japanese page) 
(in English) It is known that the footstep includes personal characteristics. We often recognize a person from walking footsteps in limited
situation. If the high accuracy personal identification using footstep is possible, a novel surveillance system like a crime prevention system, or a biometric system are expected.
In the conventional research, Shiota analyzed footsteps using a time frequency analysis and CNN. However, the pattern of the footstep waveform dataset was not used as the training data for the CNN. In addition, in the conventional research, the traditional CNN of AlexNet was used characteristic analysis. In this paper, we identify the CNN architecture of the highest accuracy by using the spectrogram and waveform dataset. Furthermore, we apply Grad-CAM to perform feature analysis.
Keyword (in Japanese) (See Japanese page) 
(in English) Footstep / CNN / Personal identification / Feature analysis / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 176, SIS2020-25, pp. 75-80, Oct. 2020.
Paper # SIS2020-25 
Date of Issue 2020-09-24 (SIS) 
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 SIS2020-25

Conference Information
Committee SIS ITE-BCT  
Conference Date 2020-10-01 - 2020-10-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) System Implementation Technology, Short Range Wireless Systems, Smart Multimedia Systems, Broadcasting Technology, etc. 
Paper Information
Registration To SIS 
Conference Code 2020-10-SIS-BCT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Future analysis of footstep waveforms and spectrogram using convolutional neural network 
Sub Title (in English)  
Keyword(1) Footstep  
Keyword(2) CNN  
Keyword(3) Personal identification  
Keyword(4) Feature analysis  
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1st Author's Name Yoshiki Goto  
1st Author's Affiliation Chubu University (Chubu Univ.)
2nd Author's Name Akitoshi Itai  
2nd Author's Affiliation Chubu University (Chubu Univ.)
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Speaker Author-1 
Date Time 2020-10-02 11:00:00 
Presentation Time 20 minutes 
Registration for SIS 
Paper # SIS2020-25 
Volume (vol) vol.120 
Number (no) no.176 
Page pp.75-80 
#Pages 6 
Date of Issue 2020-09-24 (SIS) 

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