Endotracheal suctioning is a crucial medical procedure for patients on mechanical ventilation to maintain airflow, but it is an invasive procedure that involves risks for the patient, and requires a high level of skill from the nurses who perform the procedure. Therefore, proper training and technology support are essential to minimize risks. Research by Ms. Ngo et al. focuses on recognizing nurses’ suctioning activities, aiding skill assessment. In the 6th Activity and Behavior Computing (ABC), a challenge competition aims to improve this accuracy using a dataset of key-points and annotations data generated from videos during endotracheal suctioning activity. In this competition, 22 subjects’ datasets were distributed, and participants had to recognize nine activity classes of endotracheal suctioning labeled from 0 to 8. First, we examined tendencies in subjects’ activities captured in videos of endotracheal suctioning to address misclassifications among classes 4, 5, and 0, as well as among classes 1, 2, 3, 6, 8, and 7. Specifically, due to the fixed camera angle and the subjects’ working positions during endotracheal suctioning, we attempted to improve recognition accuracy by incorporating rule-based algorithms into machine learning based on these conditions. Consequently, promising features and rules such as elapsed time, disparity between left and right hand movements, post-processing considering the sequence of endotracheal suctioning execution, among others, were identified. As a result of evaluation, we achieved macro accuracy, precision, recall, and F1-score of approximately 0.859, 0.773, 0.767, and 0.738, respectively. Additionally, we augmented the data of 1, 3, 5, and 6 classes with ChatGPT-4 to improve the activity recognition accuracy of these classes. As a result of the evaluation, an improvement in the recognition accuracy of classes 1, 3, 5, and 6 was observed, finally, the macro activity recognition accuracy, precision, recall and F1-score were 0.858, 0.778, 0.793 and 0.749, respectively.
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