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Leveraging the Large Language Model for Activity Recognition: A Comprehensive Review
http://hdl.handle.net/10228/0002001237
http://hdl.handle.net/10228/0002001237253bf0bc-5340-423b-b3eb-050a37b70cac
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
10444552.pdf (970 KB)
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| アイテムタイプ | 共通アイテムタイプ(1) | |||||||||||
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| 公開日 | 2025-02-05 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Leveraging the Large Language Model for Activity Recognition: A Comprehensive Review | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
Milyun Ni’ma Shoumi,
× Milyun Ni’ma Shoumi,
× 井上, 創造
WEKO
27425
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| 著作権関連情報 | ||||||||||||
| 権利情報 | Copyright (c) 2024 Author | |||||||||||
| 著作権関連情報 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/deed.ja | |||||||||||
| 権利情報 | This article is licensed under a Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/deed.ja | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | In this paper, we are using comprehensively review the ways in which Large Language Models (LLMs) advance activity recognition systems, discuss the challenges of implementing LLMs, and compare results between LLM-based methods and traditional approaches. We study the basic concepts of LLMs, subsequently, we systematically analyze the researches that have used LLMs for activity recognition, along with the areas related to these tasks, including object detection and speech recognition, since activity recognition can incorporate object detection and speech recognition techniques in its process to improve accuracy and provide a more comprehensive contextual understanding of human activities. We analyze the insights from 26 related research works using the Systematic Literature Review (SLR) approach. By synthesizing recent research, this review shows that LLMs can be applied in various stages of the activity recognition process, where 10% of surveyed paper are implemented at the data collection stage, 10% at the data preprocessing stage, 50% at the feature extraction stage, and 30% papers at the model training stage. Therefore, the data collection and data preprocessing stages allow for more in-depth exploration of opportunities to integrate LLMs at both stages. Moreover, LLMs offer several advantages over traditional methods, including efficient feature extraction, superior performance compared to widely used techniques, robustness across a wide range of data sets, and important enhancements that lead to state-of-the-art performance. | |||||||||||
| 言語 | en | |||||||||||
| 備考 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | 6th International Conference on Activity and Behavior Computing, ABC2024, May 28-31, 2024, Nakatsu and Kitakyushu, Kyushu, Japan (Hybrid) | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : International Journal of Activity and Behavior Computing 巻 2024, 号 2, p. 1-27, 発行日 2024-06-13 |
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| 出版社 | ||||||||||||
| 出版者 | 九州工業大学ケアXDXセンター | |||||||||||
| 言語 | ja | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 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 | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| DOI | ||||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.60401/ijabc.21 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2759-2871 | |||||||||||
| 会議記述 | ||||||||||||
| 会議名 | 6th International Conference on Activity and Behavior Computing, ABC2024 | |||||||||||
| 言語 | en | |||||||||||
| 回次 | 6 | |||||||||||
| 開始年 | 2024 | |||||||||||
| 開始月 | 05 | |||||||||||
| 開始日 | 28 | |||||||||||
| 終了年 | 2024 | |||||||||||
| 終了月 | 05 | |||||||||||
| 終了日 | 31 | |||||||||||
| 開催国 | JPN | |||||||||||
| 研究者情報 | ||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/140_ja.html | |||||||||||
| 論文ID（連携） | ||||||||||||
| 値 | 10444552 | |||||||||||
| 連携ID | ||||||||||||
| 値 | 12749 | |||||||||||
