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| Presentation | 2016-08-24 13:00 Adaptation Methods for Daily Activity Recognition Based on Deep Neural Network Tomoki Hayashi (Nagoya Univ.), Norihide Kitaoka (Tokushima Univ.), Tomoki Toda, Kazuya Takeda (Nagoya Univ.)  SP2016-27 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Our objective is to build a monitoring system which enables elderly people to live actively, and the key technology to achieve it is acitivity recognition. In our previous study, we confirmed that the effectiveness of Deep Neural Networks using multi-model signal in the subject-closed condition. However, for practical use, it is necessary to achive good performance in the subject open condition. In this study, we collected a dataset of over 300 hours of 19 subject’s activities recorded in real situations and use it to evaluate in the subject open condition. Futhermore, we introduce adaptation methods, which have been studied for long time in the field of speech recognition, into activity recognition, to improve its performance. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Daily Activity Recognition / Deep Neural Network / Adaptation / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 189, SP2016-27, pp. 1-6, Aug. 2016. | |
| Paper # | SP2016-27 | |
| Date of Issue | 2016-08-17 (SP) | |
| ISSN | Print edition: ISSN 0913-5685 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 | SP2016-27 |
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| Conference Information | |
| Committee | SP |
| Conference Date | 2016-08-24 - 2016-08-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | ACCMS, Kyoto Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Audio event processing, etc. |
| Paper Information | |
| Registration To | SP |
| Conference Code | 2016-08-SP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Adaptation Methods for Daily Activity Recognition Based on Deep Neural Network |
| Sub Title (in English) | |
| Keyword(1) | Daily Activity Recognition |
| Keyword(2) | Deep Neural Network |
| Keyword(3) | Adaptation |
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| 1st Author's Name | Tomoki Hayashi |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | Norihide Kitaoka |
| 2nd Author's Affiliation | Tokushima University (Tokushima Univ.) |
| 3rd Author's Name | Tomoki Toda |
| 3rd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 4th Author's Name | Kazuya Takeda |
| 4th Author's Affiliation | Nagoya University (Nagoya Univ.) |
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| Speaker | Author-1 |
| Date Time | 2016-08-24 13:00:00 |
| Presentation Time | 25 minutes |
| Registration for | SP |
| Paper # | SP2016-27 |
| Volume (vol) | vol.116 |
| Number (no) | no.189 |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2016-08-17 (SP) |
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