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| Presentation | 2019-03-18 10:45 ADL Recognition based on Movement Trajectory of Hand and Object Recognition using Deep Learning for Self-Support Hiroki Kojima (OIT), Sho Ooi (Rits), Mutsuo Sano (OIT)  BioX2018-51 PRMU2018-155 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In our laboratory, we have been studying cognitive rehabilitation for patients with acquired brain injury and dementia based on activities daily living (ADL) such as cooking and clearing. We proposed a method of cleaning behavior estimation using deep learning and conducted experiments of cleaning behavior to patients with acquired brain injury. Specifically, we estimated cleaning behavior using cleaning tools and cleaning area (person's direction). Cleaning tools are recognized by YOLO algorithm, person's directions are recognized by OpenPose algorithm. However, in this method, there were many misrecognition. Therefore, we add movement trajectory of object and hand as feature and describe recognition accuracy of ADL behavior. As a result, accuracy was improved by a method of error correction method which considers time continuity when features are face orientation, hand movement, and object movement. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | ADL / Self Support / YOLO / OpenPose / Living Behavior Recognition / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 513, PRMU2018-155, pp. 131-136, March 2019. | |
| Paper # | PRMU2018-155 | |
| Date of Issue | 2019-03-10 (BioX, PRMU) | |
| 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 | BioX2018-51 PRMU2018-155 |
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| Conference Information | |
| Committee | PRMU BioX |
| Conference Date | 2019-03-17 - 2019-03-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2019-03-PRMU-BioX |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | ADL Recognition based on Movement Trajectory of Hand and Object Recognition using Deep Learning for Self-Support |
| Sub Title (in English) | |
| Keyword(1) | ADL |
| Keyword(2) | Self Support |
| Keyword(3) | YOLO |
| Keyword(4) | OpenPose |
| Keyword(5) | Living Behavior Recognition |
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| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Hiroki Kojima |
| 1st Author's Affiliation | Graduate School of Osaka Institute of Technology (OIT) |
| 2nd Author's Name | Sho Ooi |
| 2nd Author's Affiliation | Ritsumeikan University (Rits) |
| 3rd Author's Name | Mutsuo Sano |
| 3rd Author's Affiliation | Osaka Institute of Technology (OIT) |
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| Speaker | Author-1 |
| Date Time | 2019-03-18 10:45:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | BioX2018-51, PRMU2018-155 |
| Volume (vol) | vol.118 |
| Number (no) | no.512(BioX), no.513(PRMU) |
| Page | pp.131-136 |
| #Pages | 6 |
| Date of Issue | 2019-03-10 (BioX, PRMU) |
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