Arm Movement

Research on arm movement focuses on understanding and replicating its complexities, primarily for applications in robotics and rehabilitation. Current efforts utilize machine learning, particularly deep learning architectures like recurrent neural networks and generative adversarial networks, to model arm kinematics and dynamics, often incorporating physical models and sensor data (e.g., IMUs, FMG) for improved accuracy and robustness. This research is significant for advancing humanoid robotics, improving stroke rehabilitation through personalized therapies, and enabling more intuitive human-robot interaction in various settings, such as assistive technologies and manufacturing.