Human Gait

Human gait analysis focuses on understanding and quantifying human locomotion patterns, primarily to diagnose medical conditions and improve assistive technologies. Current research heavily utilizes machine learning, employing convolutional neural networks, recurrent neural networks (like LSTMs), and transformers to analyze data from various sources, including wearable sensors, video, and even smartphone data, to detect gait anomalies and predict conditions like Parkinson's disease or depression. These advancements have significant implications for healthcare, enabling earlier diagnosis of neurological disorders and personalized treatment strategies, as well as enhancing the development of more effective prosthetics and exoskeletons.