Background: This article presents the system architecture and validation of the NeuroSuitUp body–machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. Methods: The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy subjects participated in system validation trials, performing three arm and three hand exercises (each 10 motor task trials) and completing user experience questionnaires. Results: Acceptable correlation was observed in 23/30 arm exercises performed with the jacket. No significant differences in glove sensor data during actuation state were observed. No difficulty to use, discomfort, or negative robotics perception were reported. Conclusions: Subsequent design improvements will implement additional absolute orientation sensors, MARG/EMG based biofeedback to the game, improved immersion through Augmented Reality and improvements towards system robustness.

This study aims to present the development of a wearable prototype device consisting of soft robotic gloves (SRGs), its integration into a wearable robotics platform for sensorimotor rehabilitation, and the device’s validation experiments with individuals suffering from impaired hand motor function due to neurological lesions. The SRG is tested and evaluated by users with spinal cord injury (SCI) and stroke. The proposed system combines multiple-sensor arrays with pneumatic actuation to assist finger movement during grasping tasks. Evaluations on SCI and stroke patients revealed that the gloves consistently improved finger and grip performance. Detailed analyses indicated observable differences in sensor-derived features during actuation versus non-actuation, with statistically significant modifications appearing in both time-domain and frequency-domain metrics. Although the stroke participants exhibited greater variability, all participants were able to use the system reporting low discomfort and effort. The findings underscore the potential for personalized calibration to further optimize therapeutic outcomes. In summary, the study validates the utility of these gloves as assistive and rehabilitative modalities, and future research will focus on refining the device in the context of multimodal wearable robotics and individualized neurorehabilitation strategies.

The impact of digital environments on academic performance, particularly among students with ADHD, has become a significant area of study, especially in the wake of increased online learning during the pandemic. This study evaluates how ADHD symptoms affect text comprehension -including accuracy, retention of quantitative and qualitative data, and logical and working memory - within multitasking academic settings in Virtual Reality (VR) and Real Life (VIVO). In our pilot experimental study, we have assessed 30 students (20 in VR and 10 in VIVO) from Greece and Romania. Participants were divided into four groups: a Control and an Experimental group for both VR and VIVO settings. The students were tasked with one (text), two (text and audio), or three (text, audio, and content creation) simultaneous activities to simulate multitasking. We utilized the Adult ADHD Self-report Scale (ASRS) and a grid of text comprehension to measure the impacts. The findings, analyzed through descriptive statistics and non-parametric tests, revealed significant differences (p=.000, χ^2=33.036) in text comprehension based on the environment (VR vs. VIVO), and the presence of ADHD symptoms. Notably, two-thirds of the participants scored above average for ADHD symptoms. Additionally, a comparison of ADHD symptoms scores from pre-tests to post-tests showed a decreasing trend, aligning with existing literature that supports managing ADHD symptoms through psychoeducation. These results underscore the varying effects of multitasking in academic settings on students with ADHD, and highlight the distinct challenges and benefits presented by VR and VIVO learning environments.

Kinesthetic motor imagery (KMI), the mental rehearsal of a motor task without its actual performance, constitutes one of the most common techniques used for brain–computer interface (BCI) control for movement-related tasks. The effect of neural injury on motor cortical activity during execution and imagery remains under investigation in terms of activations, processing of motor onset, and BCI control. The current work aims to conduct a post hoc investigation of the event-related potential (ERP)-based processing of KMI during BCI control of anthropomorphic robotic arms by spinal cord injury (SCI) patients and healthy control participants in a completed clinical trial. For this purpose, we analyzed 14-channel electroencephalography (EEG) data from 10 patients with cervical SCI and 8 healthy individuals, recorded through Emotiv EPOC BCI, as the participants attempted to move anthropomorphic robotic arms using KMI. EEG data were pre-processed by band-pass filtering (8–30 Hz) and independent component analysis (ICA). ERPs were calculated at the sensor space, and analysis of variance (ANOVA) was used to determine potential differences between groups. Our results showed no statistically significant differences between SCI patients and healthy control groups regarding mean amplitude and latency (p < 0.05) across the recorded channels at various time points during stimulus presentation. Notably, no significant differences were observed in ERP components, except for the P200 component at the T8 channel. These findings suggest that brain circuits associated with motor planning and sensorimotor processes are not disrupted due to anatomical damage following SCI. The temporal dynamics of motor-related areas—particularly in channels like F3, FC5, and F7—indicate that essential motor imagery (MI) circuits remain functional. Limitations include the relatively small sample size that may hamper the generalization of our findings, the sensor-space analysis that restricts anatomical specificity and neurophysiological interpretations, and the use of a low-density EEG headset, lacking coverage over key motor regions. Non-invasive EEG-based BCI systems for motor rehabilitation in SCI patients could effectively leverage intact neural circuits to promote neuroplasticity and facilitate motor recovery. Future work should include validation against larger, longitudinal, high-density, source-space EEG datasets.