From caretaking activities for elderly people to being assistive in healthcare setup, mobile and non-mobile robots have the potential to be highly applicable and serviceable. The ongoing pandemic has shown that human-to-human contact in healthcare institutions and senior homes must be limited. In this scenario, elderlies and immunocompromised individuals must be exclusively protected. Robots are a promising way to overcome this problem in assisted living environments. In addition, the advent of AI and machine learning will pave a way for intelligent robots with cognitive abilities, while enabling them to be more aware of their surroundings. In this paper, we discuss the general perspectives, potential research opportunities, and challenges arising in the area of robots in assisted living environments and present our research work pertaining to certain application scenarios, i.e., robots in rehabilitation and robots in hospital environments and pandemics, which, in turn, exhibits the growing prospects and interdisciplinary nature of the field of robots in assisted living environment.

Driven by the latest advancements in wireless technology, location-based services have attracted the interest of computing and telecommunication industries, as well as academia, to launch fast and accurate localization systems. The aim of this work is to propose a closed-loop localization framework for large-scale deployments, facilitating both the modeling and continuous monitoring of Activities of Daily Living (ADLs). The proposed system learns from a minimal set of Received Signal Strength Indicator (RSSI) samples, enriches them to cover unmeasured distances, and keeps recalibrating itself with live data. This method delivers a 0.5–0.8 m mean error, improving the error reported in recent studies by 65%. Furthermore, once reliable position estimation is achieved, the proposed framework can detect predefined Activities of Daily Living (ADLs) based on location patterns and movement behaviors, achieving 91% accuracy. This capability opens new opportunities for context-aware services and smart environment applications. Each module of the framework was individually tested and evaluated, demonstrating strong performance both in isolation and as part of the integrated system.

Driven by the latest advancements in wireless technology location-based services have attracted the interest of computing and telecommunication industries, as well as academia, to launch fast and accurate localization systems. The aim of this work is to propose a closed-loop localization framework for large-scale deployments facilitating both the modeling and continuous monitoring of Activities of Daily Living (ADLs). The design of these localization systems is very challenging, time consuming and their adaptation in environmental changes is hard. The proposed methodology takes advantage of limited RSSI measurements at different distances, enriches the data and accurately models the attenuation of the propagated signal. These measurements are then used as input in the data-enrichment process, where the proposed framework generates datasets at different distances. Therefore, all created datasets (gathered and generated) are exploited to train the proposed ML-based chain. The primary purpose of the ML-chain is to determine the distance between the mobile nodes and each installed beacon. The position is then calculated using trilateration methods. Finally, the collected RSSI along with the estimated position will be stored and used for increasing position accuracy, allowing our proposed framework to continuously and automatically optimize its processes and accuracy. Furthermore, to be useful and practical, once reliable position estimation is achieved, the proposed framework can detect predefined Activities of Daily Living (ADLs) based on location patterns and movement behaviors. This capability opens new opportunities for context-aware services and smart environment applications. Each module of the framework was individually tested and evaluated, demonstrating strong performance both in isolation and as part of the integrated system.

Extended Reality (XR) applications for emergency medical response impose stringent latency requirements that commodity fifth-generation (5G) deployments do not satisfy by default. This paper presents EMERGE-XR, a real-time XR system delivering AI-driven medical guidance to first responders, and addresses the research problem of how Medium Access Control (MAC)-layer parameter tuning on a software-defined 5G gNodeB (gNB) can reliably achieve sub-10 ms round-trip time (RTT) for interactive XR workloads. The system integrates Meta Quest 3 augmented reality (AR) headsets, edge-hosted pose estimation, and a clinical decision-support workflow based on the ABCDE trauma assessment protocol. The clinical guidance component has been functionally demonstrated on the laboratory testbed but has not yet been evaluated in a user study; the experimentally validated contributions of this work concern the network-layer optimization and the profile-switching mechanism, not the clinical or AI-driven components. Through systematic MAC-layer optimization on an on-premises Amarisoft 5G gNB, tuning Scheduling Request (SR) periodicity, the Time Division Duplex (TDD) uplink/downlink slot pattern, and fixed MAC timing parameters, the average RTT was reduced from 24 ms to 9 ms, a 62.5% improvement, with zero packet loss. A lightweight RTT-driven profile-switching mechanism was validated across more than 100 experimental runs. As a work-in-progress contribution, an AI-driven intent-based extension is further proposed, but not yet experimentally validated, in which a contextual reinforcement learning (RL) agent autonomously selects MAC-layer profiles based on application-level Quality of Experience (QoE) signals and network Key Performance Indicators (KPIs). Planned validation over the 6G-BRICKS Cell-Free Massive MIMO testbed at KU Leuven is outlined.