Human Activity Recognition focuses on developing systems and techniques to recognise and categorise human actions automatically based on sensor data. This study combines ultra-wideband technology and binary sensors to describe and recognise daily activities in real-world environments with multiple occupants, ensuring accurate user localisation through non-invasive and privacy-respecting methods. A novel method that combines wearables with ultra-wideband technology, which allows the generation of heatmaps for accurate positioning, and binary sensors, which collect nearby interaction with daily activities in naturalistic conditions, is presented. A dataset comprised of real-world data collected from three individuals in a real-life environment (house) was compiled. Advanced deep learning models are implemented to effectively fuse spatiotemporal information, leading to an encouraging performance in recognition of daily activities. The promising results suggest that this approach could be viable for large-scale deployments in future smart environments.

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 $5.0\times 4.8$ 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.

Breast cancer is the most lethal type of cancer among women, one of the causes can be due to the lack of professionals to evaluate the results of medical images in time (Sharafaddini et al. Multimed Tools Appl, 1–112 2024). This problem is even greater in developing countries. In recent years, diagnostic tools based on artificial intelligence techniques have been developed to improve diagnosis time and results. In this work, we present a system that analyzes histopathological images obtained from breast tissue biopsies to design a classification system that distinguishes between benign and malignant tissue. To demonstrate that the proposed work is robust, multiple alternatives and combinations are studied to obtain the best cases. Finally, we compare the proposed approach with previous works. Furthermore, the developed system integrates explainable artificial intelligence techniques to produce a report to the physician, including a heat map with the areas the system has determined to be essential for classification.