Toward Recognizing Nursing Activity in Endotracheal Suctioning Using Video-based Pose Estimation
-
- Hoang Anh Vy Ngo
- Kyushu Institute of Technology
-
- Quynh N Phuong Vu
- Kyushu Institute of Technology
-
- Noriyo Colley
- Hokkaido University
-
- Shinji Ninomiya
- Hiroshima International University
-
- Satoshi Kanai
- Hokkaido University
-
- Shunsuke Komizunai
- Kagawa University
-
- Atsushi Konno
- Hokkaido University
-
- Misuzu Nakamura
- Jikei University School of Medicine
-
- Sozo Inoue
- Kyushu Institute of Technology
Abstract
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%.
Journal
-
- International Journal of Activity and Behavior Computing
-
International Journal of Activity and Behavior Computing 2024 (1), n/a-, 2024-05-01
Care XDX Center, Kyushu Institute of Technology
- Tweet
Details 詳細情報について
-
- CRID
- 1390581544797501568
-
- DOI
- 10.60401/ijabc.1
-
- ISSN
- 27592871
-
- Text Lang
- en
-
- Data Source
-
- JaLC
-
- Abstract License Flag
- Allowed