Building Energy and Comfort Management (BECM) systems have the potential to considerably reduce costs related to energy consumption and improve the efficiency of resource exploitation, by implementing strategies for resource management and control and policies for Demand-Side Management (DSM). One of the main requirements for such systems is to be able to adapt their management decisions to the users’ specific habits and preferences, even when they change over time. This feature is fundamental to prevent users’ disaffection and the gradual abandonment of the system. In this paper, a sensor-based system for analysis of user habits and early detection and prediction of user activities is presented. To improve the resulting accuracy, the system incorporates statistics related to other relevant external conditions that have been observed to be correlated (e.g., time of the day). Performance evaluation on a real use case proves that the proposed system enables early recognition of activities after only 10 sensor events with an accuracy of $81\%$ . Furthermore, the correlation between activities can be used to predict the next activity with an accuracy of about $60\%$ .

The healthcare landscape is undergoing a transformative shift driven by the rapid advancements of sixth-generation (6G) cellular networks and Internet of Things (IoT) technologies. This convergence has led to the emergence of medical digital twins (MDTs) — innovative virtual replicas of physical entities or processes. MDTs enable safe monitoring and testing of therapeutic decisions, resulting in substantial cost savings and enhanced decision-making capabilities for physicians. Furthermore, they provide real-time assistance and facilitate comprehensive data analysis, revolutionizing patient care. However, their full potential is constrained by the intricate nature of human life cycles. Key factors include the choice of data types and the communication protocols used to connect physical and digital twins. For individuals with disabilities, integrating wearable devices with edge computing and WiFi-based Channel State Information (CSI) can effectively monitor health metrics and recognize activities. Although the integration of 6G and IoT technologies holds the promise of enabling seamless communication between physical and digital twins, this paper, which is developed in the framework of the HIPPOCRATES project, focuses on a currently implementable architecture for MDTs based on the Message Queuing Telemetry Transport (MQTT) protocol. The proposed system integrates CSI to augment health monitoring and intervention, particularly for individuals with disabilities.