Gait Analysis

Gait analysis studies human locomotion to understand movement patterns and identify abnormalities, primarily aiming for improved diagnosis and treatment of various conditions. Current research heavily utilizes computer vision and machine learning, employing deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, often coupled with explainable AI (XAI) techniques to enhance model transparency. These advancements enable markerless gait analysis in diverse settings, offering cost-effective and accessible tools for clinical applications, such as diagnosing neurological diseases, scoliosis, and lower extremity injuries, and for monitoring rehabilitation progress.