Summary of the Nurse Care Activity Recognition Challenge Using Skeleton Data from Video with Generative AI
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- Hoang Anh Vy Ngo
- 九州工業大学
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- Haru Kaneko
- 九州工業大学
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- Iqbal Hassan
- 九州工業大学
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- Elsen Ronando
- 九州工業大学
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- Milyun Ni’ma Shoumi
- 九州工業大学
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- Ryuya Munemoto
- 九州工業大学
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- Tahera Hossain
- 名古屋大学
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- Sozo Inoue
- 九州工業大学
説明
In this paper, we summarize the outcomes of a challenge we organized, where participants were tasked with evaluating nursing performance in endotracheal suctioning (ES) through Human Activity Recognition (HAR) using skeleton and video data combined with Generative AI, aiming to enhance training and improve healthcare delivery. Endotracheal suctioning is a critical procedure in intensive care units, essential for clearing pulmonary secretions from patients with artificial airways, but it carries risks such as bleeding and infection. To aid nursing training programs by evaluating performance during ES, we organized the Activity Recognition of Nurse Training Activity using Skeleton and Video Dataset with Generative AI, as part of the 6th International Conference on Activity and Behavior Computing. Participants were tasked with recognizing 9 activities in ES using skeleton data, with a requirement to utilize Generative AI creatively. The dataset included recordings of ten experienced nurses with over three years of clinical suctioning experience and twelve nursing students from a university performing ES. The challenge, which took place from January 17th to March 23rd, 2024, was assessed based on the average F1 score for all subjects and the quality of the submitted pa- pers. Therefore, Team Seahawk achieved the highest F1 score of 57% by leveraging ChatGPT for feature suggestion, LightGBM for classification, and Optuna for hyperparameter optimization, significantly surpassing the baseline 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 (3), 1-20, 2024
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詳細情報 詳細情報について
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- CRID
- 1390019833533486720
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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