This exploratory study analyses parameters influencing a tele-rehabilitation program using the Virtual Reality Rehabilitation System (VRSS) HomeKit for patients with Parkinson’s disease (PD). Data from 10 patients with idiopathic PD who completed 20 upper limb motor exercise sessions were analysed to assess correlations between system-generated metrics and clinical outcomes. Patients were clinically assessed with the Fugl-Meyer Assessment (FMA) and the Unified Parkinson’s Disease Rating Scale (UPDRS). In the experiments, we focused on two macro categories of exercises performed by patients, i.e., reaching and catching, while analysing how correlations among patients’ age, clinician-provided Hoehn-Yahr (HY) stage, and VRRS-provided metrics (such as repetitions, mean duration, correct responses, and omission errors) influenced exercise score assessments and other potential outcomes. This study is propaedeutic for advanced ML-based analyses to model patient progress and predict outcomes throughout treatment, laying the foundation for patient-centric precision medicine and the development of tailored remote rehabilitation strategies.

This work compares univariate and multivariate Long Short-Term Memory (LSTM) models for predicting delays in public transportation. Only historical delay data was used to train the univariate model, which is effective for last-minute predictions but limited in capturing wider temporal correlations because it is optimized for real-time inference with minimal data requirements. To better capture intricate patterns in transportation delays, the multivariate model incorporates extra contextual factors like time of day and topographical coordinates. Multiple multivariate models were trained on different time intervals (e.g., daily, weekly, and monthly) to evaluate the impact of training data selection on predictive accuracy. The findings show that multivariate models provide better long-term accuracy, especially when trained on properly segmented data, whereas univariate models are computationally efficient and excellent for short-term updates. These results suggest how to best adapt LSTM-based forecasting models for dynamic, practical transportation applications.

Remote patient monitoring is a form of telehealth that allows medical centres to monitor and manage their patients’ chronic conditions. Often, depending on the severity of the disease, patients can experience either temporary or permanent home hospitalization. Although the classical medical approach involves continuous monitoring of vital parameters through specialized medical devices, it does not allow observation of patient’s behaviours, which may provide additional information of interest to physicians. In this context, digital biomarkers represent the next frontiers towards precision medicine. In this paper, we explore the possible adoption of Audio Biomarkers for monitoring the behaviours of long-term home hospitalized patients. In particular, we trained and tested several Machine Learning (ML) models to recognise different sounds (i.e., sneezing, breathing, coughing, snoring, teeth brushing, and toilet flush). The results show that, even with a few audio samples, the considered models provide good performance.

In the last few years, the healthcare industry has seen significant advances in medical image analysis, mainly driven by the substantial progress of Deep Learning (DL). Convolutional Neural Networks (CNNs) have been the reference model for image-processing tasks. Recently, however, the advent of Vision Transformers (ViTs) has challenged their dominance. In this work, we explore the potential of ViTs for pneumonia diagnosis, comparing their performance with CNNs using different learning approaches. Specifically, we assessed the behaviour of From-Scratch Learning (FSL) and Pre-Trained (PT) models, leveraging Transfer Learning (TL), to highlight their performance differences. Experiments are performed in a Microsoft Azure Cloud laboratory considering both centralised and distributed Federated Learning (FL) scenarios, proving that the latter helps to mitigate the potential biases contained in the dataset, achieving similar accuracies and reducing training times linearly with the number of clients.

In recent years, significant progress has been achieved in medical image analysis, mainly due to the substantial advances in deep learning methods. In the past decade, Convolutional Neural Network (CNN) was the best model for image classification, demonstrating remarkable success in various medical applications. However, the advent of Vision Transformers (ViTs) has challenged the dominance of CNN approaches. This study aims to explore the potential of ViTs in healthcare, comparing their performance with that of CNN models. The latter has traditionally excelled in image feature extraction through convolutional operations; on the other hand, ViTs, relying on self-attention mechanisms, exhibit unique capabilities in capturing long-range dependencies, enabling them to effectively capture complex patterns within images. In this study, after analyzing their architectures, we assessed the behaviour of from-scratch and pre-trained models, highlighting their differences in performance and providing light on the applicability of Transfer Learning (TL) approach in the healthcare scenario.