In collaborative robotics, to improve human–robot interaction (HRI), it is necessary to avoid accidental impacts. In this direction, several works reported how to modify the trajectories of collaborative robots (cobots), monitoring the operator’s position in the cobot workspace by industrial safety devices, cameras, or wearable tracking devices. The detection of the emotional state of the operator could further prevent possible dangerous situations. This work aimed to increase the predictability of anomalous behavior on the part of human operators by the implementation of emotional intelligence (EI) that allows a cobot to detect the operator’s Level of Attention (LoA), implicitly associated with the emotional state, and to decide the safest trajectory to complete a task. Consequently, the operator is induced to pay due attention, the safety rate of the HRI is improved, and the cobot downtime is reduced. The approach was based on a vision transformer (ViT) architecture trained and validated by the Level of Attention Dataset (LoAD), the ad hoc dataset created and developed on facial expressions and hand gestures. ViT was integrated into a digital twin of the Omron TM5-700 cobot, suitably developed within this project, and the effectiveness of the EI was tested on a pick-and-place task. Then, the proposed approach was experimentally validated with the physical cobot. The results of the simulation and experimentation showed that the goal of the work was achieved and the decision-making process can be successfully integrated into existing robot control strategies.

Soft actuators have enabled the growth of soft robotics, overcoming several drawbacks of rigid robotics by providing devices with many degrees of freedom and the ability to grasp, bend, move, jump, and more. The reconfiguration of the workspace is still a limitation of these actuators. Indeed, once the actuator is designed and developed, it is used for a specific task. This work presents a reconfigurable soft pneumatic actuator with a novel reconfigurable modular reinforcement. The latter is wrapped around an inner tube in silicone rubber and is made of components whose assembly can be configured based on the task. A formulation is identified by a hybrid approach based on finite element analysis and response surface methodology for predicting and designing the behavior of the actuator. The prototyping revealed the ease of fabrication and reconfigurability as the strength of this new actuator. The experimental tests demonstrated the feasibility of adopting the actuator as a finger in a gripper for handling and moving objects of different shapes, masses, and stiffness. Furthermore, the evaluated performance shows a good trade-off between mass, developed force, implementation time, easy reconfigurability, and cost-effectiveness.

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.

The advent of collaborative and soft robotics has reduced the mandatory adoption of safety barriers, pushing human–robot interaction to previously unreachable levels. Due to their reciprocal advantages, integrating these technologies can maximize a device’s performance. However, simplifying assumptions or elementary geometries are often required due to non-linear factors that identify analytical models for designing soft pneumatic actuators for collaborative and soft robotics. Over time, various approaches have been employed to overcome these issues, including finite element analysis, response surface methodology (RSM), and machine learning (ML) algorithms. Based on the latter, in this study, the bending behavior of an externally reinforced soft pneumatic actuator was characterized by the changing geometric and functional parameters, realizing a Bend dataset. This was used to train 14 regression algorithms, and the Bilayered neural network (BNN) was the best. Three different external reinforcements, excluded for the realization of the dataset, were tested by comparing the predicted and experimental bending angles. The BNN demonstrated significantly lower error than that obtained by RSM, validating the methodology and highlighting how ML techniques can advance the prediction and mechanical design of soft pneumatic actuators.

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.