The global demographic shift toward an aging population presents a critical socio-economic challenge, necessitating "aging in place" solutions that balance autonomy with safety. Although the Internet of Medical Things (IoMT) offers a theoretical foundation for remote monitoring, current implementations often fail to meet real-world requirements due to high costs, intrusive sensing modalities, and a lack of contextual reasoning. This article outlines the architectural requirements of the next-generation platform for digital health support. We argue that the future of monitoring the elderly lies within the framework of Agentic Artificial Intelligence (AI), a system that not only records events but also reasons about them, detects and adapts to anomalies, and communicates with caregivers through natural language. As a result, the next generation of digital wellness platforms must bridge the gap between technical data and human understanding, providing high-precision detection, human-readable, and context-aware recommendations. This shifts systems from simple data loggers to proactive decision-supporting tools.

Augmented reality (AR) applications greatly benefit from direction estimation. In current AR devices, direction estimation is enabled by power hungry technologies such as GPS, management of inertial data and video processing. We present a low-complexity eyewear prototype capable of obtaining directional information from a set of environmental radio sources by using a multi-antenna system patented by Wagoo LLC. Eyewear devices are the natural match for this technology under the assumption that user line of sight represents the main reference for user interaction and orientation. In the demonstration, the prototype detects when a user aims at objects or people that are associated to standard Bluetooth Low Energy emitters (beacons) with a current 30° sensitivity and 5% error rate.