Virtual reality (VR) and augmented reality (AR) are engaging interfaces that can be of benefit for rehabilitation therapy. However, they are still not widely used, and the use of surface electromyography (sEMG) signals is not established for them. Our goal is to explore whether there is a standardized protocol towards therapeutic applications since there are not many methodological reviews that focus on sEMG control/feedback. A systematic literature review using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology is conducted. A Boolean search in databases was performed applying inclusion/exclusion criteria; articles older than 5 years and repeated were excluded. A total of 393 articles were selected for screening, of which 66.15% were excluded, 131 records were eligible, 69.46% use neither VR/AR interfaces nor sEMG control; 40 articles remained. Categories are, application: neurological motor rehabilitation (70%), prosthesis training (30%); processing algorithm: artificial intelligence (40%), direct control (20%); hardware: Myo Armband (22.5%), Delsys (10%), proprietary (17.5%); VR/AR interface: training scene model (25%), videogame (47.5%), first-person (20%). Finally, applications are focused on motor neurorehabilitation after stroke/amputation; however, there is no consensus regarding signal processing or classification criteria. Future work should deal with proposing guidelines to standardize these technologies for their adoption in clinical practice.

Wheelchairs are vital for the successful rehabilitation and inclusion of people with mobility disabilities; 10% of the population with disabilities needs a wheelchair, but only 15% have access to a...

Trunk control (TC) constitutes one of the main objectives in the rehabilitation of people with a spinal cord injury (SCI), but there are few clinically validated tests to assess it. Accelerometers ...

Background: Despite advances in myoelectric control of hand prostheses, their dropout rate remains high. Methods: We analyzed 37 features extracted from surface electromyography (sEMG) recordings from 15 participants, distributed into three groups: non-impaired individuals, impaired individuals with limb loss due to trauma, and impaired individuals with limb loss due to electrical burn. Feature relationships were examined with correlation heatmaps and two feature-selection methods (ReliefF and Minimal Redundancy Maximum Relevance), and classification performance was evaluated using machine-learning models to characterize sEMG behavior across groups. Results: Individuals with electrical-burn injury exhibited increased forearm co-contraction on the affected side across normalized isometric contractions, indicating altered motor coordination and likely higher energetic cost for prosthetic control. Feature selection and model results revealed etiology-dependent differences in the most informative sEMG features, underscoring the need for personalized, etiology-aware myoelectric control strategies. Conclusions: These findings inform the design of adaptive prosthetic controllers and targeted rehabilitation protocols that account for injury-specific motor control adaptations.