Wearable Gait Assessment for Diabetes: A Systematic Survey

This systematic review examines how gait analysis has been applied to understand, detect, and manage diabetes and its complications, with a focus on wearable sensor technologies and computational methods. A total of 30 studies were identified from IEEE Xplore, Scopus, and Google Scholar databases using systematic search and screening processes. Data extraction followed a structured framework addressing research questions on gait applications, technologies, and associated parameters. Results indicate that wearable sensor technologies, coupled with advanced computational modeling and machine learning, can capture meaningful gait alterations associated with long-term metabolic dysregulation and neuropathic changes. Applications range from diabetic neuropathy detection and foot ulcer prevention to intervention evaluation and early biomarker identification. The review highlights current progress and outlines future directions toward predictive gait analytics that may serve as indirect, secondary markers of metabolic status and improve diabetes care outcomes. Furthermore, this synthesis provides evidence for integrating wearable gait assessment into diabetes management protocols, potentially enabling early detection of complications, personalized intervention strategies, and non-invasive monitoring approaches that complement traditional glucose measurements.