Intelligent Rehabilitation Systems Based on Big Data Analytics and Artificial Intelligence: A Systematic Review

Abstract

The world population is aging at an accelerating pace, and the rate of disability caused by chronic diseases and trauma is on the rise. The traditional rehabilitation model has some structural defects, such as subjective evaluation, homogeneity in treatment schemes, imbalance of resource allocation, and insufficient intervention accuracy. The deep integration of big data analysis, artificial intelligence (AI), edge computing, digital twins, federated learning and multimodal large models is promoting a shift in the rehabilitation system from an experience-driven paradigm to a data-driven paradigm. This review systematically summarizes the development and evolution of big data analysis and artificial intelligence pertaining to intelligent rehabilitation, proposes a four-tier progressive technical architecture, summarizes the standardized governance paradigm of heterogeneous rehabilitation big data, and analyzes the mechanism and application boundaries of artificial intelligence algorithms in rehabilitation scenarios, such as neurology, orthopedics, elderly balance, and speech cognition. By comparing typical intelligent rehabilitation platforms horizontally, the core bottlenecks, including data islands, lack of clinical interpretability, weak robustness in complex environments and lack of evidence-based support, were identified. A future development path for integrating federated learning, lightweight multimodal models, human digital twins, flexible wearable sensing and other technologies is proposed to provide theoretical support for the next generation of rehabilitation systems.