Toward Recognizing Nursing Activity in Endotracheal Suctioning Using Video-based Pose Estimation

Hoang Anh Vy Ngo, Quynh N Phuong Vu, Noriyo Colley, Shinji Ninomiya, Satoshi Kanai, Shunsuke Komizunai, Atsushi Konno, Misuzu Nakamura, Sozo Inoue

doi:10.60401/ijabc.1

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%.

Toward Recognizing Nursing Activity in Endotracheal Suctioning Using Video-based Pose Estimation

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