Next-Generation Neurological Physiotherapy: Deep Learning–Based Movement Profiling and Real-Time Personalized Rehabilitation Algorithms

Abstract

Neurological physiotherapy is entering a pivotal phase where conventional protocol-driven care is increasingly misaligned with the complexity and heterogeneity of real-world disability. Patients with stroke, traumatic brain injury, spinal cord injury, Parkinsonian syndromes, and other central nervous system disorders present with highly individualized movement impairments that evolve over time. Yet most rehabilitation pathways remain anchored to coarse clinical scales and therapist observation, which—although essential—cannot fully capture millisecond-level timing, subtle compensations, or distributed whole-body coordination. Deep learning–based movement profiling and closed loop personalized rehabilitation algorithms offer a credible route to next-generation practice. This article proposes a multimodal framework that combines wearable inertial sensors, pressure insoles, and depth cameras with spatiotemporal feature extraction and deep learning encoders to derive a stable, low-dimensional “movement signature” for each patient. These signatures drive real-time controllers that adapt task difficulty, assistance, feedback modality, and dosing during therapy, while explicitly keeping the physiotherapist in the loop as the clinical decision-maker. The paper outlines a pragmatic research protocol, including model architecture, data pipeline, physiotherapy intervention design, outcome measures, sample size estimation, and ethical safeguards. Particular emphasis is placed on interpretability, therapist trust, safety mechanisms, and equitable deployment in low-resource settings. Rather than framing algorithms as a replacement for human expertise, the proposed system is designed to extend a therapist’s sensory bandwidth, standardize high-quality care, and create a continuously learning rehabilitation ecosystem. This concept paper provides a technically grounded yet clinically oriented roadmap that researchers can translate into multicentre trials and implementation studies.

Keywords: Neurological Physiotherapy; Deep Learning; Movement Profiling; Personalized Rehabilitation; Wearable Sensors; Closed-Loop Control; Digital Biomarkers; Precision Neurorehabilitation

Citation

Muthukrishnan P, Rajadurai S. Next Generation Neurological Physiotherapy: Deep Learning–Based Movement Profiling and Real-Time Personalized Rehabilitation Algorithms. WebLog J Neurol. wjn.2025.l1802. https://doi.org/10.5281/zenodo.18055550