In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one’s knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel.

Gesture recognition has a crucial role in Human–Computer Interaction (HCI) and in assisting the elderly to perform automatically their everyday activities. In this paper, three methods for gesture recognition and computer vision were implemented and tested in order to investigate the most suitable one. All methods, machine learning using IMU, machine learning on device, and were combined with certain activities that were determined during a needs analysis research. The same volunteers took part in the pilot testing of the proposed methods. The results highlight the strengths and weaknesses of each approach, revealing that while some methods excel in specific scenarios, the integrated solution of MoveNet and CNN provides a robust framework for real-time gesture recognition.

In this work, an IoT system with edge computing capability is proposed facilitating postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises are executed correctly, which is critical since, it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages Internet of Things (IoT) paradigm, in combination with Deep Learning and Edge Computing to classify the extend-flex movement of one’s knee via embedded Machine Learning (ML) classification algorithms. The contribution of the proposed work is multilayered. Furthermore as an outcome of this work a dataset of labeled knee movements is freely available on https://www.kaggle.com/datasets/billskarm/knee-range-of-motion to the research community. It also provides real time movement detection with an accuracy reaching 100%, which is achieved with a ML model trained to fit a low cost, off the shelf, Bluetooth Low Energy platform. The proposed Edge Computing approach allows predictions to be performed on-device rather than solely relying on a cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy, while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99,9% wireless data transfer reduction, a critical 39% prediction delay reduction and a valuable 17% increase of prediction event. Finally, enhanced privacy comprises another significant benefit from the implemented Edge Computing ML model as sensitive data can be processed on-site and only events or predictions are shared with the medical personnel.

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.

Gesture recognition has a crucial role in Human-Computer Interaction (HCI) and in assisting the elderly to perform automatically their everyday activities. In this paper three methods for gesture recognition and computer vision were implemented and tested in order to investigate the most suitable one. All methods, Machine learning using IMU, Machine learning on device and were combined with certain activities that were determined during a needs analysis research. The same volunteers took part to the pilot testing of the proposed methods. The results highlight the strengths and weaknesses of each approach, revealing that 60 while some methods excel in specific scenarios, the integrated solution of MoveNet and 61 CNN provides a robust framework for real-time gesture recognition.