Skeleton Data

Skeleton data, representing the spatial and temporal arrangement of human joints, is increasingly used for a variety of applications, primarily focusing on action recognition, gait analysis, and human-computer interaction. Current research emphasizes developing efficient and accurate models, often employing graph convolutional networks (GCNs), transformers, and multilayer perceptrons, to analyze this data, with a strong focus on addressing challenges like low-quality data, open-set recognition, and privacy concerns. These advancements are driving improvements in areas such as healthcare (fall detection, depression screening), assistive robotics (motion learning), and human-computer interaction (gesture recognition), highlighting the growing importance of robust and efficient skeleton data processing.