Abstract
This research is based on the 6th ABC Challenge which focuses on leveraging Human Activity Recognition (HAR) systems to enhance Endotracheal Suctioning (ES) procedures. The challenge’s objective is to accurately identify the activities performed by nurses based on the dataset. The dataset comprising skeleton data and video recordings of healthcare professionals performing ES procedures is collected and preprocessed. Informative features capturing joint angles, velocities, and spatial relationships are extracted. These features are then used as inputs to three different prediction models GBDT, XGBoost, and LightGBM. Our experimental results demonstrate that LightGBM outperforms the other models with the highest accuracy of 0.819, followed by XGBoost (0.807) and GBDT (0.763) on the Nurse Care Activity Recognition Challenge benchmark dataset. These findings contribute to advancing nurse activity recognition and have implications for improving healthcare monitoring and workflow management. Given the outstanding performance of LightGBM, we chose to submit our results using this algorithm for the challenge. The code is available at https://github.com/mobaaa12/Endotracheal-Suctioning-Procedure-Recognition.
https://ror.org/011ashp19 
