Skeleton Based

Skeleton-based analysis focuses on extracting meaningful information from human skeletal data for various applications, primarily aiming to understand human movement and interaction. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), transformers, and graph convolutional networks (GCNs) to process skeletal data represented as graphs, point clouds, or images, often incorporating techniques like attention mechanisms and adaptive graph structures. This field is significant for its potential in diverse areas, including human action recognition, gait analysis for security and healthcare, sign language recognition, and even robotics applications like path planning and 3D printing. The development of robust and efficient models for analyzing skeletal data is driving progress across these domains.