The Use of Hybrid Systems in Neurorehabilitation: A Narrative Review - Djordjevic, Olivera; Radic, Ana; Vidakovic, Aleksandra; Mitrovic, Sindi; Dedijer Dujovic, Suzana; Dimkic Tomic, Tijana; Konstantinovic, Ljubica (Medical Research, 2026)
The use of hybrid systems in neurorehabilitation represents a significant advancement in the field, integrating multiple technological and therapeutic approaches to enhance recovery outcomes for patients with neurological impairments. Recent technological advancements have given rise to hybrid neurorehabilitation systems—integrative platforms that combine biological signals and artificial technologies to drive more effective and personalized rehabilitation This multifaceted approach addresses the complex nature of neural damage and plasticity, offering personalized and adaptive rehabilitation protocols. Hybrid systems in neurorehabilitation commonly integrate modalities such as brain-computer interfaces, robotics, functional electrical stimulation and virtual reality. These systems leverage multimodal sensory feedback and advanced control algorithms to promote neuroplasticity, functional reorganization, and motor relearning by actively involving the patient’s neural and muscular activity in real-time therapeutic interventions. Clinical studies have demonstrated that hybrid systems can improve motor functions in stroke survivors, spinal cord injury patients, and individuals with neurodegenerative diseases more effectively than conventional rehabilitation alone. These systems offer quantifiable performance metrics, allowing clinicians to tailor interventions and monitor progress objectively. Furthermore, the technology enables intensive, repetitive, and task-specific practice, which are critical factors in neural recovery. Overall, hybrid systems in neurorehabilitation exemplify the convergence of neuroscience, engineering, and clinical therapy, resulting in innovative solutions that maximize functional recovery. Future developments will likely focus on increasing system accessibility, optimizing user interfaces, and integrating machine learning algorithms to refine adaptive rehabilitation strategies further