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

Xiaoyan Li, Cuicui Jiang, Zhenzhen Xie, Li Liu

Abstract:

Traditional nursing activity recognition relies on
manual observation and documentation, which are time-
consuming and error-prone. This paper presents CareAtten-
Net, 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, to-
taling 7,631,843 temporal measurements. CareAttenNet achieved
77.36% validation accuracy and 60.00% test accuracy, outper-
forming baseline architectures including CNN-LSTM (57.92%),
Correlation-Aware CNN (54.62%), and Feature-Selective Net-
work (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 inter-
actions between architectural modules. These findings provide
insights into multi-modal sensor fusion challenges and establish
a foundation for intelligent healthcare monitoring systems.
Index Terms—Nursing activity recognition, Wearable senso