A comprehensive survey on diagnosis and assessment of Parkinson's disease via plantar pressure analysis.

Parkinson's disease (PD) is a progressive neurodegenerative disorder. Its motor symptoms include bradykinesia, rigidity, and postural instability, while gait abnormalities serve as critical early biomarkers. Traditional diagnostic methods normally rely on subjective clinical assessments, highlighting the need for objective, quantifiable tools. Plantar pressure measurement systems provide high-resolution spatiotemporal and dynamic load data, directly reflecting foot-ground biomechanical interactions and offering insights into balance control and disease progression. This review comprehensively surveys plantar pressure analysis for PD diagnosis and assessment, encompassing data acquisition, existing datasets, preprocessing methodologies, feature engineering, and neural-network-based diagnostic models. We highlight approaches employing artificial intelligence (AI), which have been extensively adopted with promising outcomes for gait-based PD diagnosis and monitoring. Finally, challenges including heterogeneous comorbidities and the interpretability and ethical issues of AI are discussed, alongside future directions in telemonitoring and edge computing. By bridging biomechanics and AI, this survey underscores the transformative potential of plantar pressure analysis for early, objective, and personalized PD diagnostics. Future research should integrate edge intelligence with wearable sensing to develop wearable, continuous monitoring and intervention systems. Simultaneously, multi-center collaborative studies should validate the universal applicability of these systems in real-world clinical settings.