Spatiotemporal Gait

Spatiotemporal gait analysis focuses on understanding human walking patterns by analyzing both spatial (e.g., step length, joint angles) and temporal (e.g., cadence, gait cycle duration) aspects of movement. Current research heavily utilizes deep learning, particularly recurrent neural networks (RNNs) and convolutional neural networks (CNNs), often combined, to extract gait features from various data sources like wearable sensors and video. These analyses aim to improve medical diagnosis (e.g., detecting gait disorders), monitor disease progression, and enable applications such as fall prediction and personalized rehabilitation. The field's impact stems from its potential to provide objective, quantitative measures of gait for clinical and assistive technologies.