CareAttenNet: Deep Learning Framework with Temporal Attention for Automated Nursing Activity Recognition from Wearable Sensors

Xiaoyan Li, Cuicui Jiang, Zhenzhen Xie

Abstract: Traditional nursing activity recognition relies on manual observation and documentation, which are time-consuming and error-prone. This paper presents CareAttenNet, a deep learning framework integrating adaptive feature selection, correlation-aware processing, and temporal attention mechanisms for automated nursing activity recognition from wearable sensor data. We evaluated the framework using the SONAR dataset comprising 70-dimensional sensor features from 14 healthcare professionals performing 23 nursing activities, totaling 7,631,843 temporal measurements. CareAttenNet achieved 77.36% validation accuracy and 60.00% test accuracy, outperforming baseline architectures including CNN-LSTM (57.92%), Correlation-Aware CNN (54.62%), and Feature-Selective Network (52.86%). Ablation studies revealed temporal attention as the most effective component (78.33% test accuracy), while feature selection combined with temporal attention achieved 77.40% accuracy. However, combining all components resulted in performance degradation, indicating complex negative interactions between architectural modules. These findings provide insights into multi-modal sensor fusion challenges and establish a foundation for intelligent healthcare monitoring systems.