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Wearable Sensor-Based Gait Analysis for Age and Gender Estimation
http://hdl.handle.net/10228/00008456
http://hdl.handle.net/10228/000084568d594871-b9e1-49b6-8f95-a6664bcbb356
名前 / ファイル | ライセンス | アクション |
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sensors-20-02424-v2.pdf (3.2 MB)
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Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||
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公開日 | 2021-09-10 | |||||||||||
資源タイプ | ||||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
資源タイプ | journal article | |||||||||||
タイトル | ||||||||||||
タイトル | Wearable Sensor-Based Gait Analysis for Age and Gender Estimation | |||||||||||
言語 | ||||||||||||
言語 | eng | |||||||||||
著者 |
Ahad, Md Atiqur Rahman
× Ahad, Md Atiqur Rahman× Ngo, Thanh Trung× Antar, Anindya Das× Ahmed, Masud× Hossain, Tahera× Muramatsu, Daigo× Makihara, Yasushi× 井上, 創造
WEKO
27425
× Yagi, Yasushi |
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network. | |||||||||||
書誌情報 |
Sensors 巻 20, 号 8, p. 2424-1-2424-24, 発行日 2020-04-24 |
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出版社 | ||||||||||||
出版者 | MDPI | |||||||||||
DOI | ||||||||||||
関連タイプ | isIdenticalTo | |||||||||||
識別子タイプ | DOI | |||||||||||
関連識別子 | https://doi.org/10.3390/s20082424 | |||||||||||
日本十進分類法 | ||||||||||||
主題Scheme | NDC | |||||||||||
主題 | 501 | |||||||||||
ISSN | ||||||||||||
収録物識別子タイプ | ISSN | |||||||||||
収録物識別子 | 1424-8220 | |||||||||||
著作権関連情報 | ||||||||||||
権利情報 | Copyright (c) 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | gait | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | recognition | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | wearable sensor | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | age estimation | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | gender | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | smartphone | |||||||||||
出版タイプ | ||||||||||||
出版タイプ | VoR | |||||||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
査読の有無 | ||||||||||||
値 | yes | |||||||||||
研究者情報 | ||||||||||||
https://hyokadb02.jimu.kyutech.ac.jp/html/140_ja.html | ||||||||||||
論文ID(連携) | ||||||||||||
10379296 | ||||||||||||
連携ID | ||||||||||||
9263 | ||||||||||||
著者別名 | ||||||||||||
姓名 | Inoue, Sozo | |||||||||||
言語 | en | |||||||||||
姓名 | 井上, 創造 | |||||||||||
言語 | ja | |||||||||||
姓名 | イノウエ, ソウゾウ | |||||||||||
言語 | ja-Kana |