Training Gesture Classification System

Training gesture classification systems focuses on developing accurate and robust methods for recognizing and interpreting human gestures from various data sources, aiming to enable natural human-computer interaction and improve assistive technologies. Current research emphasizes the impact of data dimensionality (2D vs. 3D) on model performance, exploring deep learning architectures like transformers and recurrent neural networks for processing sequential gesture data, and investigating the use of robot-collected data to augment limited human datasets. These advancements hold significant implications for diverse fields, including human-robot interaction, healthcare (e.g., assistive robotics for individuals with motor impairments), and virtual/augmented reality applications.