Joint Trajectory

Joint trajectory analysis focuses on understanding and modeling the movement patterns of body joints, aiming to extract meaningful information from these trajectories for various applications. Current research emphasizes developing robust methods for capturing and analyzing joint trajectories from diverse data sources, including real-time MRI, surface electromyography (sEMG), and video, often employing machine learning techniques like neural networks (including U-Net and contrastive learning approaches) and wavelet transforms to improve accuracy and efficiency. These advancements have significant implications for fields like biomechanics, rehabilitation engineering (e.g., prosthetic limb control), and human-computer interaction, enabling more precise assessments of movement disorders, improved design of assistive devices, and more natural human-machine interfaces.