Skeleton Encoder

Skeleton encoders are machine learning models designed to extract meaningful representations from human skeletal data, primarily for applications like action recognition and gait analysis. Current research emphasizes improving the accuracy and generalizability of these encoders through techniques such as contrastive learning, multi-modal fusion (combining skeleton data with visual or textual information), and the incorporation of large language models to guide feature extraction. These advancements are driving progress in diverse fields, including robotics, healthcare (e.g., Parkinson's disease diagnosis), and human-computer interaction, by enabling more accurate and efficient analysis of human movement.