Human-Robot Interaction Evaluation-Based AAN Control for Upper Limb Rehabilitation Robots Driven by Series Elastic Actuators

Series elastic actuators (SEAs) have been the most popular compliant actuators as they possess a variety of advantages, such as high compliance, good backdrivability, and tolerance to shocks. They have been adopted by various rehabilitation robots to provide appropriate assistance with suitable compliance during human-robot interaction. For a multijoint SEA-driven rehabilitation robot, a big challenge is to develop an assist-as-needed (AAN) method without losing stability during uncertain physical human-robot interaction. For this purpose, this article proposes a human-robot interaction evaluation-based AAN method for upper limb rehabilitation robots driven by SEAs. First, in order to stabilize the SEA-level dynamics, singular perturbation theory is adopted to design a fast time-scale controller. Second, for the robot-level dynamics, an iterative learning algorithm is adopted for impedance adaption according to the task performance and human intention. The interaction force feedback is introduced for human-robot interaction evaluation, and the intensity of robotic assistance will be adjusted periodically according to the evaluation results. The stability of human-robot interaction is provided with the Lyapunov method. Finally, the proposed rehabilitation method is constructed and implemented on a two-degree-of-freedom SEA-driven robot. It handles the uncertain interaction in such a principle that correct movements will lead to less assistance for encouraging participation and incorrect movements will lead to more assistance for effective training. The proposed method adapts to the subject's intention and encourages higher participation by decreasing impedance learning strength and increasing allowable motion error. It can fit the participants with different motor capabilities and provide adaptive assistance when a specific trainee tries to change his/her participation during rehabilitation. The performance of the AAN method was validated with experimental studies involving healthy subjects.

Automated summary

The article proposes an assist-as-needed method for upper limb rehabilitation robots driven by series elastic actuators (SEAs) based on human-robot interaction evaluation. The method provides stable human-robot interaction through the design of a controller and the application of an iterative learning algorithm, as well as periodically adjusting the intensity of robotic assistance according to the evaluation results. The method adapts to different participants' abilities and provides adaptive assistance when a specific trainee tries to change their participation.