| Item type |
共通アイテムタイプ(1) |
| 公開日 |
2025-02-05 |
| タイトル |
|
|
タイトル |
Nurses' Skill Assessment in Endotracheal Suctioning Using Video-based Activity Recognition |
|
言語 |
en |
| 著者 |
Hoang, Anh Vy Ngo
Colley, Noriyo
Ninomiya, Shinji
Kanai, Satoshi
Komizunai, Shunsuke
Konno, Atsushi
Nakamura, Misuzu
井上, 創造
|
| 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 present an approach to assess nurses’ skills by using activity recognition in the context of Endotracheal Suctioning (ES) performed by nurses which is an important nursing activity. Our proposed structure for skill assessment hinges on three aspects: the activity order, suction time, and the smoothness exhibited during the suctioning process. Our order score algorithm works correctly in ground truth and identifies correctly mistakes on Not remembering to remove PPE before auscultation in activity recognition results compared to a professional nurse's evaluation. The recognized suction time is similar to the ground truth with only 1 to 2 seconds. The analysis of suctioning smoothness shows a similar trend to force data that nurses performed ES more smoothly by putting less pressure on the catheter than students. To recognize ES activities, we extract pose skeletons from multi-view videos, using a dataset including nurses and nursing students performing ES. Our methodology involves extracting pose skeletons from front and back views and enhancing model performance with skip frames, post-processing, and using micro labels for training, then evaluating with macro labels. After using multi-view data and training with micro labels, our proposed method improves the accuracy by 4% and the F1-score by 9%. By combining multi-view pose extraction, advanced post-processing, and a nuanced skill assessment framework, our work contributes to advancing activity recognition in endotracheal suctioning, fostering a deeper understanding of nurses' proficiency in this critical medical procedure. |
|
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. 1-24,
発行日 2024-06-13
|
| 出版社 |
|
|
出版者 |
九州工業大学ケアXDXセンター |
|
言語 |
en |
| 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.20 |
| 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 |
10444550 |
| 連携ID |
|
|
値 |
12742 |
10444550.pdf (4.6 MB)
