DIGITAL NEUROREHABILITATION IN CONTEMPORARY NEUROLOGY: TECHNOLOGICAL FOUNDATIONS, CLOSED-LOOP SYSTEMS, AND CLINICAL IMPLEMENTATION

Abstract

Neurological disorders—led by ischemic and hemorrhagic stroke, traumatic brain injury (TBI), spinal cord injury (SCI), and neurodegenerative conditions such as Parkinson’s disease and multiple sclerosis—represent a foremost contributor to global disease burden, long-term functional disability, and socioeconomic strain. While conventional physical and occupational therapies remain standard pillars of neurorestorative care, their real-world impact is frequently constrained by low repetition doses, subjective clinical rating scales, high workforce dependency, and poor long-term adherence following hospital discharge. Digital neurorehabilitation has emerged as a transformative, systems-level paradigm that shifts restorative neurology from empirical, episodic regimens toward continuous, high-dose, and data-driven motor re-education. This comprehensive review synthesizes empirical and technological advances (2019–2026) across the digital neurorehabilitation ecosystem. We examine the biomedical and engineering foundations of robotic exoskeletons and end-effector devices, immersive virtual, augmented, and mixed reality environments (VR/AR/MR), non-invasive electroencephalography-based brain-computer interfaces (BCIs), and closed-loop multi-sensor feedback architectures. Furthermore, we analyze the decentralization of therapy through wearable sensor networks and tele-rehabilitation platforms, alongside macroeconomic cost-effectiveness, medical device regulatory frameworks, and systemic clinical integration challenges. By bridging computational systems engineering, neurophysiology, and clinical neurology, this review establishes that closed-loop digital neurorehabilitation represents an indispensable, scalable pillar of modern precision neurological recovery.