Body Gesture

Body gesture research focuses on understanding and replicating human nonverbal communication through movement, encompassing both hand gestures and full-body movements. Current research employs various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs and GRUs), and transformers, often combined with techniques like self-supervised learning and pose estimation (e.g., using OpenPose) to analyze and generate gestures. This field is significant for advancing human-robot interaction (HRI), enabling more intuitive and natural communication with robots and other intelligent systems, as well as for applications in autism detection and assistive technologies for the visually impaired.