Gait Representation

Gait representation focuses on developing effective computational methods to capture and analyze human walking patterns for various applications, including biometric identification, healthcare diagnostics, and activity monitoring. Current research emphasizes multimodal approaches, fusing data from sources like cameras, LiDAR, and inertial measurement units, often employing deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers to extract robust and discriminative features from gait data. These advancements are improving the accuracy and robustness of gait analysis across diverse environments and conditions, with significant implications for fields ranging from security and surveillance to the diagnosis and monitoring of neurological disorders.