Enhancing Nursing Activity Recognition During Endotracheal Suctioning Through Video-based Pose Estimation and Machine Learning

Endotracheal suctioning is a crucial medical process where skilled professional nurses are needed. However, there is a lack of research on automatically recognizing nurse activities during this procedure. This paper presents an innovative method to identify nurses’ actions during endotracheal suctioning procedures by analyzing skeleton data from image sequences using different traditional machine learning-based (ML) methods. Firstly, we preprocess the skeleton data and extract the feature, then employ the ML method for classification. Moreover, we explored the Generative AI with LLM for feature generation and selection to improve the accuracy. We evaluated our proposed framework using metrics such as accuracy and F1-Score. We demonstrate our proposed framework on the Activity Recognition of Nurse Training Activity of the 6th ABC Challenge dataset and find out that XGBoost obtained the best accuracy. The accuracy and F1-score both are 97%. We hope this research contributes to automated nursing activity recognition, potentially benefiting patient care and safety during endotracheal suctioning procedures.