Gait Impairment

Gait impairment research focuses on identifying and quantifying deviations from normal walking patterns, primarily to aid in early diagnosis and monitoring of neurological disorders and age-related decline. Current research employs diverse methods, including machine learning models (like convolutional neural networks and graph attention networks) analyzing data from various sensors (accelerometers, radar, video) to classify gait patterns and predict fall risk. These advancements offer the potential for improved diagnostic tools, personalized interventions, and remote patient monitoring, ultimately enhancing healthcare for individuals experiencing gait difficulties.