The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as “Smart Parking”, “Environmental Behavior Analysis on Traffic Incidents”, and “Emergency Management”, we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity.

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.

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.