Artificial Intelligence in the Assessment of Spasticity and Gait Disorders: A Critical Review of Methods, Sensors, and Clinical Integration

Spasticity and neuromuscular dysfunctions are common consequences of neurological conditions such as cerebral palsy, stroke, traumatic brain injury, and spinal cord lesions, substantially impairing mobility and quality of life. Artificial intelligence (AI) is increasingly employed to process surface electromyography (sEMG) signals, inertial-sensor recordings, gait-analysis data, and clinical information, supporting more objective characterization of neuromuscular impairments. Following PRISMA guidelines, this systematic review synthesizes evidence from 78 studies published between 2010 and 2026, identified through an expanded multi-database search of Scopus, PubMed/MEDLINE, and IEEE Xplore. Most studies focused on lower-limb applications, followed by upper-limb assessment, whereas a smaller proportion addressed general neuromuscular impairments. Classification was a predominant AI task across the reviewed literature. Most studies relied on private or non-public datasets, limiting reproducibility and external validation, while small or single-center cohorts and heterogeneous methodological protocols were also frequent limitations. This review synthesizes methodological trends, dataset variability, and clinical applicability and identifies persistent gaps in generalizability, multimodal and longitudinal integration, and clinically validated decision-support capabilities. Overall, AI shows substantial potential to support more objective and individualized neurorehabilitation; however, broader external validation, standardized data acquisition, and prospective clinical evaluation remain necessary before widespread clinical integration.