WEKO3
アイテム
On-Device Deep Personalization for Robust Activity Data Collection
http://hdl.handle.net/10228/00008461
http://hdl.handle.net/10228/000084611dfa4dbb-6f5b-4eb7-a1a2-cc8b505d88dc
| 名前 / ファイル | ライセンス | アクション |
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LaSEINE-2020_20.pdf (3.3 MB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||
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| 公開日 | 2021-09-14 | |||||||||||||
| 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 | On-Device Deep Personalization for Robust Activity Data Collection | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 著者 |
マイリッタ, ナッタヤ
× マイリッタ, ナッタヤ× マイリッタ, ティッタヤ× 井上, 創造
WEKO
27425
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | One of the biggest challenges of activity data collection is the need to rely on users and keep them engaged to continually provide labels. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. This study proposes a novel on-device personalization for data labeling for an activity recognition system using mobile sensing. The key idea behind this system is that estimated activities personalized for a specific individual user can be used as feedback to motivate user contribution and improve data labeling quality. First, we exploited fine-tuning using a Deep Recurrent Neural Network to address the lack of sufficient training data and minimize the need for training deep learning on mobile devices from scratch. Second, we utilized a model pruning technique to reduce the computation cost of on-device personalization without affecting the accuracy. Finally, we built a robust activity data labeling system by integrating the two techniques outlined above, allowing the mobile application to create a personalized experience for the user. To demonstrate the proposed model’s capability and feasibility, we developed and deployed the proposed system to realistic settings. For our experimental setup, we gathered more than 16,800 activity windows from 12 activity classes using smartphone sensors. We empirically evaluated the proposed quality by comparing it with a baseline using machine learning. Our results indicate that the proposed system effectively improved activity accuracy recognition for individual users and reduced cost and latency for inference for mobile devices. Based on our findings, we highlight critical and promising future research directions regarding the design of efficient activity data collection with on-device personalization. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : Sensors 巻 21, 号 1, p. 41-1-41-22, 発行日 2020-12-23 |
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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/s21010041 | |||||||||||||
| 日本十進分類法 | ||||||||||||||
| 主題Scheme | NDC | |||||||||||||
| 主題 | 548 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 1424-8220 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||||||
| 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 (CCBY) license (https://creativecommons.org/licenses/by/4.0/). | |||||||||||||
| 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 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/140_ja.html | |||||||||||||
| 論文ID（連携） | ||||||||||||||
| 値 | 10379313 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 9290 | |||||||||||||
