Gait Detection

Gait detection research focuses on automatically identifying and classifying patterns in human movement, primarily using wearable sensors like accelerometers. Current efforts concentrate on improving the accuracy and real-time capabilities of gait analysis, employing machine learning models such as Long Short-Term Memory networks (LSTMs), Transformers, and ensemble methods like BagStacking, often combined with convolutional neural networks (CNNs) for feature extraction. These advancements have significant implications for healthcare, enabling improved monitoring of conditions like Parkinson's disease (specifically freezing of gait) and enhancing the functionality of prosthetic limbs and exoskeletons; furthermore, gait analysis is finding applications in security through gait recognition.