This study aims to assess the impact of different visual perturbations on user balance within an immersive Virtual Environment (VE), emphasising ecological validity. Utilising a Virtual Reality headset, the researchers implemented various visual perturbations to discern their effects. The objective was to create a versatile tool featuring animations simulating diverse indoor and outdoor settings within a typical household, providing a range of ecological scenarios for evaluating user balance in the context of these visual disturbances. Participants were exposed to unpredictable visual disturbances within the VE, and their responses were captured using a combination of inertial sensors, electromyography and galvanic skin response. A comprehensive dataset replicating real-world fall scenarios on balance disturbances was collected. Throughout the trials, participants moved around the house, unaware of the timing or nature of visual perturbations. Statistical analysis identified variables with significant mean changes when visual disturbances were introduced. Muscle groups crucial for balance recovery following external disturbances exhibited the most substantial associations, akin to kinematic variables linked to loss of balance. Visual disturbances alone could induce balance disturbances, which were further categorized based on a statistical analysis. This tool offered a versatile platform for assessing balance under various challenging scenarios, facilitating research into balance control and training methods. The dataset generated through this protocol enabled a wide understanding of the impact of visual disturbances on balance and the associated compensatory reactions, shedding light on their potential applications in clinical and rehabilitative settings.

Background/Objectives: Wearable technology is increasingly used to provide biofeedback in physical rehabilitation; however, there is no consensus on which biofeedback parameter is most appropriate for clinical use, as most studies evaluate only one arbitrarily selected parameter. This study presents a wearable multimodal biofeedback system integrating multiple parameters selected based on the prior literature and evaluates its feasibility, usability, and implementation within a rehabilitation context through a longitudinal post-stroke case study. Methods: The system integrates inertial and electromyographic sensors to monitor centre of mass (CoM-B), joint angle (ANG-B), and muscle activity (EMG-B), delivering real-time sensory cues based on the monitored parameters. Feasibility was assessed in a post-stroke participant (male, 32 years, 29 months post-stroke, left hemiparesis, Fugl-Meyer Lower Extremity Score = 27) across 15 sessions involving stand-to-sit, split-stance weight shifting, and walking tasks. Each task was practiced with all three biofeedback parameters, with five sessions per parameter. Results: The motor performance varied across biofeedback parameters and tasks. CoM-B was associated with favourable trends in motor performance during stand-to-sit, showing improvements in medio-lateral displacement (0.03/session); ANG-B during walking, showing increased ankle dorsiflexion (1 deg/session); and EMG-B during split-stance weight shifting, showing increased tibialis anterior activation (5 µV/session). Conclusions: The findings generate the hypothesis that the ability of biofeedback to elicit favourable motor performance is task-dependent, suggesting that the choice of biofeedback parameters may need to be adapted to task demands. The system demonstrated high usability and feasibility, supporting its potential for post-stroke rehabilitation. Further studies are needed to test the generated hypothesis and evaluate the system efficacy.

This study aims to evaluate multiple feature sets composed of sensor-based biomarkers acquired during walking for the automated estimation of post-stroke motor impairment levels using Fugl-Meyer Lower Extremity Assessment (FMA-LE)-derived classes. Sensor-based walking data from the open-source ARRA dataset were combined with data collected at the Hospital of Braga. Data from 32 post-stroke individuals (FMA-LE motor score: 24 ± 3) were included. A decision tree classifier was evaluated using stratified six-fold cross-validation across different feature sets, including: correlated with motor impairment levels versus full feature sets; spatiotemporal versus surface electromyographic (sEMG) features; inclusion of demographic variables; and the use of data augmentation. The best performance was achieved using correlated sEMG features combined with age, paretic side, and body mass, along with noise-based data augmentation, yielding a validation Matthews Correlation Coefficient (MCC) of 0.85 ± 0.16 and a test MCC of 0.70. sEMG features provided improved classification performance compared to spatiotemporal features, and comparable results were obtained using a reduced subset of muscles. These results demonstrate the feasibility of using sEMG-based features acquired during walking to classify post-stroke motor impairment levels. Feature reduction and inclusion of demographic variables may support efficient model design, while data augmentation may enhance generalization. Further validation in larger and more diverse datasets is required to assess robustness and clinical applicability.