Voluntary Control of the Hand Assistive Exoskeleton Based on the sEMG-Driven Musculoskeletal Model

This paper presents a novel voluntary control method for a hand assistive exoskeleton, leveraging an sEMG-driven musculoskeletal model to improve the performance of grasping tasks. To address the challenge of inadequate personalization in current hand exoskeleton assistance strategies, this research first establishes a musculoskeletal model based on the Hill-type framework. Through experimental calibration, a personalized musculoskeletal model is then developed, tailored to individual subjects. Subsequently, a method for estimating finger joint torque is introduced, leveraging the sEMG-driven personalized musculoskeletal model to achieve precise control. An exoskeleton controller based on admittance control is also designed, enabling users to voluntarily control the exoskeleton. By assessing the subject's voluntary effort, the proposed control method dynamically adjusts the level of exoskeleton assistance during grasping tasks. Finally, the effectiveness of both the personalized model and the voluntary control method is validated through rigorous experimental testing, demonstrating significant improvements in assistance strategy personalization and user experience.