Telerehabilitation is important for post-stroke or post-surgery rehabilitation because the tasks it uses are reproducible. When combined with assistive technologies, such as robots, virtual reality, tracking systems, or a combination of them, it can also allow the recording of a patient’s progression and rehabilitation monitoring, along with an objective evaluation. In this paper, we present the structure, from actors and functionalities to software and hardware views, of a novel framework that allows cooperation between patients and therapists. The system uses a computer-vision-based system named virtual glove for real-time hand tracking (40 fps), which is translated into a light and precise system. The novelty of this work lies in the fact that it gives the therapist quantitative, not only qualitative, information about the hand’s mobility, for every hand joint separately, while at the same time providing control of the result of the rehabilitation by also quantitatively monitoring the progress of the hand mobility. Finally, it also offers a strategy for patient–therapist interaction and therapist–therapist data sharing.

Computer vision (CV)-based systems using cameras and recognition algorithms offer touchless, cost-effective, precise, and versatile hand tracking. These systems allow unrestricted, fluid, and natural movements without the constraints of wearable devices, gaining popularity in human–system interaction, virtual reality, and medical procedures. However, traditional CV-based systems, relying on stationary cameras, are not compatible with mobile applications and demand substantial computing power. To address these limitations, we propose a portable hand-tracking system utilizing the Leap Motion Controller 2 (LMC) mounted on the head and controlled by a single-board computer (SBC) powered by a compact power bank. The proposed system enhances portability, enabling users to interact freely with their surroundings. We present the system’s design and conduct experimental tests to evaluate its robustness under variable lighting conditions, power consumption, CPU usage, temperature, and frame rate. This portable hand-tracking solution, which has minimal weight and runs independently of external power, proves suitable for mobile applications in daily life.

Brain–computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements—open/close, finger tapping, and wrist rotation—along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.

Multiple sclerosis (MS) is a complex, chronic, and heterogeneous disease of the central nervous system that affects 3 million people globally. The multifactorial nature of MS necessitates an adaptive and personalized approach to diagnosis, monitoring, and treatment. This paper proposes a novel Digital Twin for Multiple Sclerosis (DTMS) designed to integrate diverse data sources, including Magnetic resonance imaging (MRI), clinical biomarkers, and digital health metrics, into a unified predictive model. The DTMS aims to enhance the precision of MS management by providing real-time, individualized insights into disease progression and treatment efficacy. Through a federated learning approach, the DTMS leverages explainable AI to offer reliable and personalized therapeutic recommendations, ultimately striving to delay disability and improve patient outcomes. This comprehensive digital framework represents a significant advancement in the application of AI and digital twins in the field of neurology, promising a more tailored and effective management strategy for MS.

Physical rehabilitation (PR) is a critical medical discipline traditionally reliant on qualitative data for procedure evaluation. Recent scientific and technological advances have provided innovative instruments and methods for measuring and evaluating PR objectively through Quantitative PR (QPR). However, the lack of a standard data format creates several challenges. These include limited interoperability between devices, difficulties in maintaining patient histories, inability to perform temporal evaluations or inter-patient comparisons, barriers to data sharing, challenges in creating common evaluation scales for therapists, and limitations in statistical analysis. This article proposes a DICOM Information Object Definition (IOD) for QPR, referred to as PR-IOD, and describes its architecture. DICOM is an established standard initially created for medical imaging, but it has recently been extended to other areas of medicine. Its primary goals are to facilitate data sharing among various devices, manage associated processes, and ensure interoperability among systems and specialists by generating structured data. The implemented PR-IOD architecture has been applied to manage data by a multiple-source hand-tracking device, the Virtual Glove (VG), used for hand rehabilitation. The corresponding DICOM files have been generated, loaded, and visualized alongside a viewer dashboard specifically tailored for PR-IOD. The source code is available at [1].