Integrating subject-specific workspace constraint and performance-based control strategy in robot-assisted rehabilitation.

Introduction: The robot-assistive technique has been widely developed in the field of neurorehabilitation for enhancement of neuroplasticity, muscle activity, and training positivity. To improve the reliability and feasibility in this patient-robot interactive context, motion constraint methods and adaptive assistance strategies have been developed to guarantee the movement safety and promote the training effectiveness based on the user's movement information. Unfortunately, few works focus on customizing quantitative and appropriate workspace for each subject in passive/active training mode, and how to provide the precise assistance by considering movement constraints to improve human active participation should be further delved as well. Methods: This study proposes an integrated framework for robot-assisted upper-limb training. A human kinematic upper-limb model is built to achieve a quantitative human-robot interactive workspace, and an iterative learning-based repulsive force field is developed to balance the compliant degrees of movement freedom and constraint. On this basis, a radial basis function neural network (RBFNN)-based control structure is further explored to obtain appropriate robotic assistance. The proposed strategy was preliminarily validated for bilateral upper-limb training with an end-effector-based robotic system. Results: Experiments on healthy subjects are enrolled to validate the safety and feasibility of the proposed framework. The results show that the framework is capable of providing personalized movement workspace to guarantee safe and natural motion, and the RBFNN-based control structure can rapidly converge to the appropriate robotic assistance for individuals to efficiently complete various training tasks. Discussion: The integrated framework has the potential to improve outcomes in personalized movement constraint and optimized robotic assistance. Future studies are necessary to involve clinical application with a larger sample size of patients.