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| Presentation | 2020-03-13 11:00 Human motion classification by convolutional neural network using signal strength of WBAN Shintaro Sano, Aoyagi Takahiro (TokyoTech)  MICT2019-53 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this report, human motion classification by convolutional neural networks (CNNs) using the signal strength of WBANs (Wireless Body Area Networks) is investigated. In the preceding study, neural networks were used to classify motions based on manually designed time-domain features. In this report, CNN is investigated for automatic feature extraction of signal strength. For human motion classification using the received signal strength of sensor nodes attached to an upper arm and a thigh, the accuracy rate is almost the same as the accuracy rate of the previous research. This suggests that CNN is applicable to feature extraction of signal strength. By considering effects of difference of subjects given as training data on the accuracy of the classification and the relationship between the amount of training data and the accuracy of classification on test data, it is shown that performance of classification based on CNN is strongly affected by subjects used as training data. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | wireless body area networks / convolutional neural network / human motion classification / machine learning / feature extraction / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 480, MICT2019-53, pp. 5-9, March 2020. | |
| Paper # | MICT2019-53 | |
| Date of Issue | 2020-03-06 (MICT) | |
| 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 | MICT2019-53 |
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| Conference Information | |
| Committee | EMCJ MICT |
| Conference Date | 2020-03-13 - 2020-03-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kikai-Shinko-Kaikan Bldg. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | MICT |
| Conference Code | 2020-03-EMCJ-MICT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Human motion classification by convolutional neural network using signal strength of WBAN |
| Sub Title (in English) | |
| Keyword(1) | wireless body area networks |
| Keyword(2) | convolutional neural network |
| Keyword(3) | human motion classification |
| Keyword(4) | machine learning |
| Keyword(5) | feature extraction |
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| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shintaro Sano |
| 1st Author's Affiliation | Tokyo Institute of Technology (TokyoTech) |
| 2nd Author's Name | Aoyagi Takahiro |
| 2nd Author's Affiliation | Tokyo Institute of Technology (TokyoTech) |
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| Speaker | Author-1 |
| Date Time | 2020-03-13 11:00:00 |
| Presentation Time | 25 minutes |
| Registration for | MICT |
| Paper # | MICT2019-53 |
| Volume (vol) | vol.119 |
| Number (no) | no.480 |
| Page | pp.5-9 |
| #Pages | 5 |
| Date of Issue | 2020-03-06 (MICT) |
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