Finger Motion

Research on finger motion focuses on understanding and replicating the complex mechanics of finger movements for applications ranging from prosthetic hand control to human-computer interaction and medical rehabilitation. Current research employs diverse approaches, including machine learning models like recurrent spiking neural networks, diffusion models, and transformers, to decode neural signals, predict hand trajectories from video, and control robotic fingers. These advancements are improving the accuracy and efficiency of prosthetic devices, enhancing human-robot interaction, and providing new tools for analyzing human movement in clinical settings.