Representation of Human arm Dynamic Intents With an Electrical Impedance Tomography (EIT)-Driven Musculoskeletal Model for Human-Robot Interaction

Representing human arm dynamic intent is essential for effective human-robot interaction. Accurately and robustly decoding these intentions through mathematical modeling of neuromuscular processes poses significant challenges. This study introduces an electrical impedance tomography (EIT)-driven musculoskeletal model, which integrates an EIT sensing system with methods for muscle identification, parameter estimation, and musculoskeletal system modeling. Unlike existing muscle-signal techniques, EIT captures muscle activities from the anatomical cross-sectional plane, providing both activation dynamics and morphological features. We validated our method through multiDoF wrist kinematics estimation under varying contraction intensities, arm endpoint stiffness estimation, and robotic variable admittance control. Our approach achieves accuracy comparable to state-of-the-art methods while requiring fewer training samples and a more compact sensing system. The model incorporates physiological constraints, minimizing decoding errors, and ensuring interaction safety. This method enables reliable intent decoding with practical training demands. Future work will enhance the EIT system for complex tasks.