Abstract
In this paper, we present an approach to assess nurses’ skills by using activity recognition in the context of Endotracheal Suctioning (ES) performed by nurses which is an important nursing activity. Our proposed structure for skill assessment hinges on three aspects: the activity order, suction time, and the smoothness exhibited during the suctioning process. Our order score algorithm works correctly in ground truth and identifies correctly mistakes on Not remembering to remove PPE before auscultation in activity recognition results compared to a professional nurse's evaluation. The recognized suction time is similar to the ground truth with only 1 to 2 seconds. The analysis of suctioning smoothness shows a similar trend to force data that nurses performed ES more smoothly by putting less pressure on the catheter than students. To recognize ES activities, we extract pose skeletons from multi-view videos, using a dataset including nurses and nursing students performing ES. Our methodology involves extracting pose skeletons from front and back views and enhancing model performance with skip frames, post-processing, and using micro labels for training, then evaluating with macro labels. After using multi-view data and training with micro labels, our proposed method improves the accuracy by 4% and the F1-score by 9%. By combining multi-view pose extraction, advanced post-processing, and a nuanced skill assessment framework, our work contributes to advancing activity recognition in endotracheal suctioning, fostering a deeper understanding of nurses' proficiency in this critical medical procedure.
https://ror.org/02278tr80 
