Serena Moscato

Background: Developing Brain–Computer Interfaces (BCIs) for children with severe neuromotor disabilities remains challenging due to developmental neurophysiology and the high cognitive demands of motor imagery. Hybrid BCIs integrating electroencephalography (EEG) and electrooculography (EOG) can overcome these limitations by combining continuous motor-related control with reliable discrete commands. This multiple-case study investigated the feasibility of a hybrid EEG–EOG BCI integrated with a virtual reality (VR) powered wheelchair simulator for mobility training. Methods: Six participants (mean age: 13.00 ± 3.52 years; 1 female, 5 males) with severe neuromotor disabilities (i.e., Central Core Myopathy, LAMA2 related Congenital Muscular Dystrophy, Spinal Muscular Atrophy Type II, and Cerebral Palsy) completed repeated sessions using a self-paced BCI. The system combined continuous kinesthetic motor imagery (KMI) for steering and EOG-detected voluntary blinks for discrete commands. We evaluated online classification performance (accuracy, sensitivity, specificity), user workload (NASA-TLX), and simulator-related discomfort (MSAQ) under fully and semi-immersive VR conditions. Results: Across the selected session-level evaluations, mean online classification accuracy was 67%, increasing to 77% when considering only successful evaluations. Four of the six participants completed the experimental protocol, whereas two discontinued after T3 because sufficiently stable BCI control could not be achieved. NASA-TLX scores indicated increasing perceived workload but generally low frustration, while low MSAQ scores suggested good VR tolerability. No participants withdrew because of VR-related discomfort or adverse effects. Conclusions: This exploratory study provides preliminary evidence supporting the feasibility of hybrid EEG–EOG BCI control within a VR-based powered mobility simulator for children and adolescents with severe neuromotor disabilities. Although simulated navigation was feasible for four participants, substantial variability in BCI control highlights the need for further technological refinement and validation in larger cohorts.

Background: Developing Brain–Computer Interfaces (BCIs) for children with severe neuromotor disabilities remains challenging due to developmental neurophysiology and the high cognitive demands of motor imagery. Hybrid BCIs integrating electroencephalography (EEG) and electrooculography (EOG) can overcome these limitations by combining continuous motor-related control with reliable discrete commands. This multiple-case study investigated the feasibility of a hybrid EEG–EOG BCI integrated with a virtual reality (VR) powered-wheelchair simulator for mobility training. Methods: Six participants (one child, five adolescents) with severe neuromotor disabilities completed repeated sessions using a self-paced BCI. The system combined continuous kinesthetic motor imagery (KMI) for steering and EOG-detected voluntary blinks for discrete commands. We evaluated online classification performance (accuracy, sensitivity, specificity), user workload (NASA-TLX), and simulator-related discomfort (MSAQ) under fully and semi-immersive VR conditions. Results: Despite expected inter-subject variability, most participants achieved functional online VR wheelchair control, reaching a mean accuracy of 77% in successful sessions with balanced sensitivity and specificity. NASA-TLX scores indicated moderate cognitive workload but consistently low frustration. Zero dropouts and low MSAQ scores demonstrated excellent protocol tolerability, high engagement, and minimal cybersickness across both VR modalities. Conclusions: This study demonstrates the feasibility of combining hybrid EEG–EOG BCI control with VR for pediatric powered mobility training. The framework enabled functional control with high usability and minimal adverse effects, supporting the clinical potential of hybrid BCI–VR systems as accessible assistive technologies and providing a foundation for larger clinical trials.

Multiple sclerosis (MS) often leads to significant impairments in gait and mobility. Although walking braces are commonly prescribed, their real‐world effectiveness is limited by insufficient adaptation. Traditional clinical assessments rely on subjective scales and may not fully reflect daily‐life challenges. Immersive virtual reality (VR) and wearable sensors offer promising solutions for ecological monitoring, but their combined use remains unvalidated for orthotic support in MS. This interventional study examines the feasibility of a novel, VR‐based framework designed to support gait assessment with a passive walking brace. Twelve participants with MS will undergo a protocol combining four ambulatory sessions and a week‐long home‐monitoring phase. Standardized gait tests will be performed with and without the brace across both simulated scenarios and real‐world conditions. Multimodal data collected via inertial and physiological wearable sensors will characterize spatiotemporal gait parameters, balance, posture, and physiological responses, alongside standardized clinical scales and subjective questionnaires. Overall feasibility will be evaluated directly through user usability and acceptability, recruitment and retention rates, safety, protocol adherence, and data completeness. Given that this pilot study is not powered to assess clinical efficacy, statistical analysis will focus strictly on descriptive and exploratory comparisons across conditions and sessions. This translational framework aims to establish robust feasibility metrics to support the design of future, larger‐scale controlled trials. Ultimately, these preliminary observations will inform more ecologically valid, personalized rehabilitation strategies to improve the integration of assistive technologies into patients' daily lives. Trial Registration: ClinicalTrials.gov , NCT07096700. Registered July 24, 2025

The integration of data from multicenter clinical studies represents a key opportunity to enhance research quality. This potential can be further enhanced by standardizing variables and ensuring their semantic interoperability. In this work, we present the approach adopted, along with the preliminary results, to standardize a retrospective multicenter dataset collected within the METASTRA project, an EU H2022 initiative aimed at developing personalized strategies for patients with vertebral metastases. The dataset comprises 401 variables collected through electronic case report forms across four clinical centers. The proposed standardization strategy relies on mapping each variable to the most suitable HL7 FHIR resource and field, complemented by the use of SNOMED CT terminology. A modular transformation pipeline was applied to convert the raw data into FHIR resources. In this preliminary phase, we focused on a subset of 99 variables. Among these, 88% (87/99) were successfully standardized using nine FHIR resources and 177 SNOMED CT concepts. Validation queries confirmed full consistency between the original and standardized datasets, demonstrating the reliability of the process. This work contributes to creating a semantically coherent clinical knowledge base, enabling more effective data reuse and supporting evidence generation in multicenter clinical studies.

The AlmaHealthDB infrastructure provides a secure, GDPR-compliant centralized platform enabling multicentre biomedical research. Applied in the EU-funded METASTRA project, AHDB ensures interoperable, standardised, and traceable data management, fostering data-driven discoveries.

Global healthcare systems are under increasing strain to ensure long-term sustainability, necessitating a fundamental transition towards proactive, preventive healthcare strategies. Wearable sensor technologies are uniquely positioned to facilitate this paradigm shift by enabling continuous physiological and behavioral monitoring, facilitating early risk stratification, and enabling timely interventions. DigitAl lifelong pREvention (DARE) is a nationwide initiative conceived in Italy to develop and evaluate novel digital tools and services aimed at fostering innovative health promotion and disease prevention pathways, with wearable devices identified as a critical enabling technology. Herein, we present a comprehensive overview of 16 pilot investigations designed in DARE to rigorously assess the utility of wearable devices in the continuous monitoring of physical activity, joint mobility, sleep architecture, heart rate variability, nutrition, and glucose homeostasis across diverse health domains, including healthy aging, chronic disease prevention, and lifestyle modification programs targeting over 20000 participants. We meticulously detail the methodological characteristics of these study protocols, explicitly outlining the technical specifications, functional capabilities, and inherent limitations of the 18 distinct wearable devices (sourced from 12 different manufacturers) employed across these clinical trials. The breadth of applications and the heterogeneity of the target populations underscore the significant potential of wearable devices as an integral component of future digital health prevention strategies.