Gait Parameter

Gait parameter analysis focuses on quantifying aspects of human locomotion, aiming to understand movement patterns and identify abnormalities. Current research emphasizes developing accurate and efficient methods for gait parameter extraction, utilizing various approaches including markerless pose estimation systems (e.g., DeepLabCut), machine learning models (e.g., XGBoost, SVMs, and transformer networks), and physics-based simulations to improve the accuracy and physical plausibility of gait estimations from various data sources (e.g., video, inertial sensors). These advancements have significant implications for clinical diagnosis, rehabilitation, and the development of assistive technologies, enabling more objective and accessible assessments of gait impairments across diverse populations and settings.