Smart Bedside Traceability of Caregiver–Patient Interactions Using Wearables and Tiny Localization Anchors

Caregiver–patient traceability is essential for measuring care workload and interaction time in shared hospital rooms, where a single caregiver attends multiple patients and manual documentation is intrusive, time-consuming, and prone to errors. This paper proposes a non-invasive smart bedside sensing approach based on a commercial smartwatch with integrated Ultra-Wideband (UWB) radio worn by the caregiver and two compact UWB localization anchors deployed near the monitored beds. The classification approach uses a compact smartwatch feature set comprising three-axis magnetometer measurements and a four-component orientation quaternion provided by the device through Magnetic, Angular Rate, and Gravity (MARG)-based sensor fusion. No device is required to be worn by the patients. The system was evaluated in a shared hospital room measuring approximately 5.0×4.8 m, with two hospital beds separated by 1.3–1.7 m. Three datasets involving two caregiver participants were included in the evaluation. Several supervised learning approaches were evaluated, including Long Short-Term Memory (LSTM) networks, a hybrid Convolutional Neural Network plus LSTM (CNN+LSTM) architecture, Extreme Gradient Boosting (XGBoost), and a non-linear Support Vector Machine (SVM). XGBoost and SVM achieved the best overall performance, reaching a macro F1-score of 0.97. The results demonstrate that compact wrist-worn magnetic and MARG-derived orientation signals can accurately identify the attended bed, supporting scalable and objective caregiver–patient traceability in shared hospital rooms with minimal infrastructure.