A Rehabilitation Robot Control Framework for Task Trajectory Deformation and Robotic Assistance Adaption

Rehabilitation robots have exhibited great therapeutic potential for patients with physical disabilities. Current robot control strategies mostly drive the rehabilitation robot to move along a predetermined trajectory, and the impaired limb is motivated to complete training task with robotic assistance. In terms of patients with different movement capabilities, both of the task trajectory and robotic actuation should be adapted appropriately for safe and efficient rehabilitation. For example, it is hardly possible for severely impaired patients to continuously exert force to complete the task with guiding the robot; however, reduced task difficulty cannot stimulate patients' residual motor function. Furthermore, challenging more difficult task when subjects show improved performance is essential to encourage active participation, which is usually ignored in present studies. In this paper, a control framework is proposed to simultaneously adapt task trajectory and assistance intensity. A trajectory deformation algorithm is designed to generate smooth, continuous and compliant task trajectories with responding to physical human-robot interaction (pHRI). A feedback gain modification algorithm is developed for assist-as-needed (AAN) training to encourage patients' active engagement according to individual performance on completing the training tasks. Experiments are performed with a lower extremity exoskeleton prototype to demonstrate its effectiveness and superiority. © 2023 IEEE.