Hand Tracking

Hand tracking, the process of accurately locating and representing hand pose and movement in 3D space, aims to create robust and reliable systems for various applications. Current research focuses on improving accuracy and robustness, particularly in challenging scenarios like occlusion and fast movements, using techniques such as convolutional neural networks (CNNs), multimodal sensor fusion (combining vision with other modalities like EMG or ultrasound), and physically-informed models that incorporate biomechanical constraints. These advancements are driving progress in fields ranging from virtual and augmented reality to clinical applications like rehabilitation assessment and assistive technologies for individuals with disabilities.