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.

This paper outlines a new system for processing of eye-tracking data in basketball live games with two pre-trained Artificial Intelligence (AI) models. blueThe system is designed to process and extract features from data of basketball coaches and referees, recorded with the Pupil Labs Neon Eye Tracker, a device that is specifically optimized for video analysis. The research aims to present a tool useful for understanding their visual attention patterns during the game, what they are attending to, for how long, and their physiological responses, blueas is evidenced through pupil size changes. AI models are used to monitor events and actions within the game and correlate these with eye-tracking data to provide understanding into referees’ and coaches’ cognitive processes and decision-making. This research contributes to the knowledge of sport psychology and performance analysis by introducing the potential of Artificial Intelligence (AI)-based eye-tracking analysis in sport with wearable technology and light neural networks that are capable of running in real time.

Digital Twin technology in healthcare offers personalized care through advanced analytics, real-time data, and virtual models. In this paper we propose the adoption of the Digital Twin approach as a means for modeling hands, both injured and healthy, in a Double Digital Twin (DDT) using the Virtual Glove. The VG acts as a supportive rehabilitation device, capable of collecting data and reconstructing models for both the impaired and healthy hands. This study emphasizes the calibration process, which adjusts the model of the healthy hand to match the injured hand. Additionally, the transformed healthy hand model is used to guide the task with the injured system, ensuring synchronization and repeatability of exercises. Furthermore, the framework associated with DDT facilitates the analysis and quantification of the mobility of the impaired hand in comparison to the healthy one. Finally, a preliminary experiment is presented. Index Terms—Virtual Glove, Tele-Medicine, Double Digital Twin, Hand Rehabiliation, Virtual Reality

The integration of innovative technologies in animal healthcare has gained significance in recent years, aiming to enhance diagnostic capabilities across various species. In this work, we address a specific challenge within avian healthcare: the accurate counting of nucleated Red Blood Cells (RBCs). We propose a novel Cell Counter and Detector (CCD) algorithm, specifically designed for avian RBCs, which utilizes smartphones and optical microscopes for rapid and precise cell counting. Leveraging Multi-access Edge Computing (MEC) technology, the system ensures efficient data processing, privacy preservation, and ease of use through a user-friendly web interface. The results demonstrate that our method achieves an accuracy of 0.98, significantly outperforming existing approaches. Moreover, our system enables professionals to use the tools they already have without requiring expensive instruments. Our system leverages MEC to enable real-time processing and privacy-preserving data management, setting a new benchmark for avian blood diagnostics in terms of cost-effectiveness and accuracy. Beyond its immediate implications for avian healthcare, this research underscores the broader potential of technology in improving diagnostics for diverse animal species.

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.