Paper Abstract and Keywords |
Presentation |
2022-02-22 10:15
Classification of User's Device Possession Position and Behavior by Using Deep Metric Learning Rui Kitahara, Lifeng Zhang (Kyutech) ITS2021-40 IE2021-49 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
With the widespread use of smartphones, there have been efforts to classify human behavior using built-in sensors. However, most of these efforts are limited to a single location where the smartphone is held and are considered insufficient to be incorporated into actual smartphones as a system. If it can be confirmed that it is possible to classify behavior and possession location at the same time, it will be possible to change the notification method of the smartphone according to the user's situation. In this study, we acquired data from the accelerometer of a smartphone, trained it using deep metric learning, and classified the user's behavior and possession position using cosine similarity during inference. As a result, not only did we obtain the same accuracy as in the previous study even when classifying both actions and possession positions at the same time, but we also confirmed that it was possible to output untrained data as an unknown class. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Behavioral Classification / Accelerometer / Image Processing / Deep Learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 373, ITS2021-40, pp. 91-96, Feb. 2022. |
Paper # |
ITS2021-40 |
Date of Issue |
2022-02-14 (ITS, IE) |
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) |
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ITS2021-40 IE2021-49 |
Conference Information |
Committee |
IE ITS ITE-AIT ITE-ME ITE-MMS |
Conference Date |
2022-02-21 - 2022-02-22 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Image Processing, etc. |
Paper Information |
Registration To |
ITS |
Conference Code |
2022-02-IE-ITS-AIT-ME-MMS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Classification of User's Device Possession Position and Behavior by Using Deep Metric Learning |
Sub Title (in English) |
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Behavioral Classification |
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Accelerometer |
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Image Processing |
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Deep Learning |
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1st Author's Name |
Rui Kitahara |
1st Author's Affiliation |
Kyushu Institute of Technology (Kyutech) |
2nd Author's Name |
Lifeng Zhang |
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Kyushu Institute of Technology (Kyutech) |
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Speaker |
Author-1 |
Date Time |
2022-02-22 10:15:00 |
Presentation Time |
15 minutes |
Registration for |
ITS |
Paper # |
ITS2021-40, IE2021-49 |
Volume (vol) |
vol.121 |
Number (no) |
no.373(ITS), no.374(IE) |
Page |
pp.91-96 |
#Pages |
6 |
Date of Issue |
2022-02-14 (ITS, IE) |
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