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| Presentation | 2020-03-12 09:50 Analysis of meal records for understanding eating habits using large-scale recipe dataset Miyuki Kawarada, Hiroaki Karasawa (UT), Misako Sakamoto (DAH), Sosuke Amano (foo.log), Yoko Yamakata, Kiyoharu Aizawa (UT)  LOIS2019-70 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Nutrition management is very important for athletes, but difficult for amateur athletes who are not supervised by a dietitian. In recent years, applications that can easily manage meals using photos and barcodes have appeared, but the data obtained is limited, and athletes are not able to manage meals adequately. In this paper, we propose a method for analyzing eating habits by automatically classifying meal records into meal categories and creating an accurate food intake frequency questionnaire. In our proposed method, we first searched for recipes similar to text-based meal records, extracted the ingredients used, and classified the ingredients categories by hierarchical clustering. While the results of the evaluation experiments showed that the food category could be predicted with sufficient estimation accuracy, new issues were clarified as to how much detailed ingredients should be included in the FFQ answer. Experiments have also confirmed that the proposed method can visualize eating habits based on the athlete's diet category. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Lifelog / Athlete / Nutrition Management / Recipe search / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 477, LOIS2019-70, pp. 79-84, March 2020. | |
| Paper # | LOIS2019-70 | |
| Date of Issue | 2020-03-04 (LOIS) | |
| 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 | LOIS2019-70 |
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| Conference Information | |
| Committee | LOIS |
| Conference Date | 2020-03-11 - 2020-03-12 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nobumoto Ohama Memorial Hall |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | LOIS |
| Conference Code | 2020-03-LOIS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Analysis of meal records for understanding eating habits using large-scale recipe dataset |
| Sub Title (in English) | |
| Keyword(1) | Lifelog |
| Keyword(2) | Athlete |
| Keyword(3) | Nutrition Management |
| Keyword(4) | Recipe search |
| Keyword(5) | |
| Keyword(6) | |
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| Keyword(8) | |
| 1st Author's Name | Miyuki Kawarada |
| 1st Author's Affiliation | The University of Tokyo (UT) |
| 2nd Author's Name | Hiroaki Karasawa |
| 2nd Author's Affiliation | The University of Tokyo (UT) |
| 3rd Author's Name | Misako Sakamoto |
| 3rd Author's Affiliation | DOME ATHLETE HOUSE (DAH) |
| 4th Author's Name | Sosuke Amano |
| 4th Author's Affiliation | foo.log Inc (foo.log) |
| 5th Author's Name | Yoko Yamakata |
| 5th Author's Affiliation | The University of Tokyo (UT) |
| 6th Author's Name | Kiyoharu Aizawa |
| 6th Author's Affiliation | The University of Tokyo (UT) |
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| Speaker | Author-1 |
| Date Time | 2020-03-12 09:50:00 |
| Presentation Time | 25 minutes |
| Registration for | LOIS |
| Paper # | LOIS2019-70 |
| Volume (vol) | vol.119 |
| Number (no) | no.477 |
| Page | pp.79-84 |
| #Pages | 6 |
| Date of Issue | 2020-03-04 (LOIS) |
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