The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context of motor rehabilitation for groups of patients performing moderate physical routines, focused on balance, stretching, and posture. Specifically, we propose the I-TROPHYTS framework, which introduces a step-change in motor rehabilitation by advancing towards more sustainable medical services and personalized diagnostics. Our framework leverages wearable sensors to monitor patients’ vital signs and edge computing to detect and estimate motor routines. In addition, it incorporates a humanoid robot that mimics the actions of a physiotherapist, adapting motor routines in real-time based on the patient’s condition. All data from physiotherapy sessions are modeled using an ontology, enabling automatic reasoning and planning of robot actions. In this paper, we present the architecture of the proposed framework, which spans four layers, and discuss its enabling components. Furthermore, we detail the current deployment of the IoT system for patient monitoring and automatic identification of motor routines via Machine Learning techniques. Our experimental results, collected from a group of volunteers performing balance and stretching exercises, demonstrate that we can achieve nearly 100% accuracy in distinguishing between shoulder abduction and shoulder flexion, using Inertial Measurement Unit data from wearable IoT devices placed on the wrist and elbow of the test subjects.

Train characteristics identification serves a vital role in monitoring the traffic on railway infrastructures and correlating it with their structural response. It traditionally relies on camera-based systems, which raise privacy concerns, have high implementation costs, and perform variably depending on visibility conditions. To address these limitations, we present RATTLE, an IoT framework that uses audio fingerprinting to identify trains, count carriages, and estimate transit speed. Our system combines edge devices for data collection, feature extraction, and processing with cloud infrastructure for aggregation and model training. To validate our approach, we collected a comprehensive dataset of video and audio samples, annotated it with ground truth labels, and used it for evaluation in our experiments. We are also making this dataset openly available to the research community to support further advancements in the field. The framework employs a custom Convolutional Neural Network for processing mel spectrograms, achieving 96% accuracy in train identification, with a mean absolute error of 0.85 for carriage counting and 1.8 m/s for speed estimation. These results were obtained using only 500 training samples per location, with the model requiring less than 1.63 MB of storage, making it suitable for deployment on resource-limited devices.