Markerless Motion Analysis and AI-Based Platforms for Neurological Telerehabilitation: A Scoping Review

Area of Science:

• Neurology
• Rehabilitation Medicine
• Digital Health

Background:

• Telerehabilitation (TR) leverages digital technologies for remote healthcare delivery.
• Artificial intelligence (AI) advancements are enhancing TR capabilities, especially in remote monitoring and personalized treatment.
• Neurological disorders present unique challenges for continuous rehabilitation management.

Purpose of the Study:

• To conduct a scoping review of AI and markerless motion analysis (MMA) in telerehabilitation for neurological disorders.
• To synthesize current literature on AI/MMA applications in remote monitoring/assessment versus AI-based TR platforms.
• To identify the current state and future potential of AI in neurological telerehabilitation.

Main Methods:

• Scoping search conducted in March 2025 across PubMed, Embase, Scopus, and Web of Science.
• Inclusion criteria focused on articles published in the last ten years.
• Screening and full-text assessment of retrieved records to identify relevant studies.

Main Results:

• The review identified 10 studies from an initial 290 records after duplicate removal and screening.
• Evidence for MMA-based remote monitoring/assessment and AI-supported TR interventions is early-stage and heterogeneous.
• Included studies varied significantly in patient populations, outcome measures, and technological setups.

Conclusions:

• AI applications in telerehabilitation and remote monitoring for neurological disorders are nascent and diverse.
• AI-driven TR devices can provide objective metrics to support personalized neurological rehabilitation.
• Integrating AI into TR can enhance treatment continuity, intensity adaptation, and follow-up care beyond clinical settings.