Gesture Class

Gesture classification research aims to develop robust systems capable of accurately recognizing and interpreting human gestures from various modalities, including visual data (from cameras), electromyography (EMG) signals, and tactile sensor data. Current research focuses on improving accuracy and efficiency through techniques like deep learning (including convolutional neural networks and transformers), ensemble learning, and semi-supervised learning to address data scarcity issues, particularly for large gesture vocabularies. These advancements have significant implications for human-computer interaction, enabling more intuitive control of devices and systems in diverse applications such as robotics, virtual reality, and assistive technologies.