Toward Recognizing Nursing Activity in Endotracheal Suctioning Using Video-based Pose Estimation
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- Hoang Anh Vy Ngo
- 九州工業大学
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- Quynh N Phuong Vu
- 九州工業大学
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- Noriyo Colley
- 北海道大学
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- Shinji Ninomiya
- 広島国際大学
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- Satoshi Kanai
- 北海道大学
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- Shunsuke Komizunai
- 香川大学
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- Atsushi Konno
- 北海道大学
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- Misuzu Nakamura
- 東京慈恵会医科大学
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- Sozo Inoue
- 九州工業大学
説明
In this paper, we propose using pose estimation extracted from videos to recognize the nurses’ activity when doing endotracheal suctioning. Endotracheal suctioning is a sophisticated method that is very invasive and may accompany risks for patients. As home healthcare becomes more prevalent, there is an urgent need for more certified individuals who can perform endotracheal suctioning. However, quantitative research on nurse care activity recognition in endotracheal suctioning is still limited. To address this issue, our study aims to recognize 9 suctioning activities from video recordings by extracting their pose estimation to classify activities. Because the videos were taken in a real-world environment, there are some obstacles to overcome such as people in the background, and nurses standing out of the frame. Therefore, post-processing needs to be applied after estimating the pose. After pose estimation, these key points are input for Random Forest model to classify activities. Our model achieved an accuracy of 54% and F1-score of 46%.
収録刊行物
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- International Journal of Activity and Behavior Computing
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International Journal of Activity and Behavior Computing 2024 (1), n/a-, 2024-05-01
九州工業大学ケアXDXセンター
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詳細情報 詳細情報について
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- CRID
- 1390581544797501568
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- DOI
- 10.60401/ijabc.1
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- ISSN
- 27592871
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- 本文言語コード
- en
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- データソース種別
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- JaLC
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- 抄録ライセンスフラグ
- 使用可