Gait Motion Related Pattern

Gait motion analysis focuses on identifying and quantifying patterns in human walking, aiming to detect abnormalities indicative of neurological or physical conditions like stroke, Parkinson's disease, and scoliosis. Current research employs diverse methods, including sensor-based data from smart walkers and robotic vests, video-based analysis with deep learning models (e.g., convolutional neural networks), and self-supervised generative models for anomaly detection. These advancements offer potential for improved diagnostic tools, personalized rehabilitation strategies, and non-invasive screening methods, ultimately enhancing patient care and clinical practice.