Qing Miao ¹ Song Min ¹ Cui Wang ² Yifeng Chen ^2*

• ¹ Wuhan Polytechnic University, Wuhan, China
• ² Southern University of Science and Technology, Shenzhen, Guangdong Province, China

Robot-assistive technique has been widely developed in the field of neurorehabilitation for its enhancement to neuroplasticity, muscle activity and training positivity. In order 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 user's movements 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 constraint to improve human active participation should be further delved as well. This study proposes an integrated framework for robot-assisted upper limb training. A human kinematics upper limb model is built to achieve 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. Experimental results with human subjects indicate that the proposed strategy can guarantee safe human motion and providing appropriate assistance.