抄録
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%.
https://ror.org/02278tr80 
