Abstract
This work aims to explore the application of deep learning models in nursing activity recognition to enhance the accuracy of nurse action recognition. It mainly analyzes a specific nursing activity Endotracheal Suctioning (ES), a medical procedure requiring high precision. To improve the accuracy of daily performance assessments of nursing students performing ES, the research team tested the performance of Feedforward Neural Networks (FNN) and Deep Residual Networks (ResNet) models on this task. The results indicate that the ResNet model, compared to traditional methods and the FNN model, shows superior efficacy in handling complex temporal data and activity recognition tasks without requiring manual feature design typical of traditional models. By employing generative AI techniques, the team was prompted to use data imputation and segmentation methods to optimize model performance further. By evaluating and analyzing model performance, this study not only improves the accuracy of nursing activity recognition with 89% accuracy in 9 different types of ES activities but also demonstrates the potential for deep learning technology's future applications in the nursing field. Additionally, this shows how using generative AI in research can lead to new ways to solve real-world scientific problems. Code is available at https://github.com/lpy888999/6th-Nurse-Care-ABC.
https://ror.org/04w9fbh59 
