A Systematic Review on Gait Analytics in Disorders With Motor Phenotypes Utilizing Inertial Measurement Units and Plantar Pressure Sensors

Among the 97 studies included in this systematic review, 81 (83.5%) investigate Parkinson’s Disease (PD), reflecting the broader research landscape and the established utility of wearable sensors in capturing PD-specific gait impairments rather than an intentional PD-focused search strategy. Building on this context, this review synthesizes recent advances in gait analytics using Inertial Measurement Units (IMU) and Plantar Pressure Sensors (PPS) across disorders with motor phenotypes. Gait analysis has emerged as a critical tool for understanding motor dysfunction, enabled by portable sensor technologies capable of precise, real-time measurements in naturalistic settings. This review, spanning publications from 2020 to April 2024, examines studies identified through four major databases (PubMed, Scopus, IEEE Xplore, and Google Scholar). Eligible works were original, peer-reviewed studies applying quantitative gait analytics using IMUs and/or PPS in Arthritis, Diabetes Mellitus, or PD. Exclusion criteria comprised non-human studies, incompatible sensing modalities, use of more than ten different sensors, sensor-comparison–only studies, absence of gait protocols, and cohorts consisting solely of healthy individuals or those with gait-altering deformities. Together, IMUs and PPS are reshaping diagnostic and monitoring strategies for PD, Arthritis, and Diabetes, where gait abnormalities and neuropathic complications are central to functional impairment. Beyond PD, the review includes 12 Diabetes and 4 Arthritis studies, representing peripheral nerve and musculoskeletal conditions that remain comparatively underexplored. The included studies are synthesized by sensor modality, placement, analytical methodology, protocol design, cohort characteristics, and performance outcomes. Approaches ranged from statistical analysis to machine learning, deep learning, and rule-based models, with several reporting state-of-the-art results. The findings underscore the value of wearable sensors for monitoring disease progression, detecting gait-phase abnormalities, and supporting personalized interventions. Despite these advancements, significant limitations persist, including a lack of publicly available datasets, limited external validity, small or imbalanced samples, inconsistent sensor placement, and data integration challenges. Future directions emphasize standardized protocols, multi-center validation, and AI-driven multimodal fusion to unlock scalable, clinically reliable gait-analytics solutions across healthcare and rehabilitation.