Learning robotic motion with mirror therapy framework for hemiparesis rehabilitation

The number of hemiplegic patients has rapidly increased in recent years, and intelligent robots have high rehabilitation potential. Robotic mirror therapy (RMT) is a promising therapeutic measure for hemiparesis by voluntarily transferring the motion of the healthy limb (HL) to the impaired limb (IL), in which a robot interacts with and assists the IL to mimic the action of the HL to stimulate the active participation of the injured muscles. Nonetheless, complete replication of the HL movement trajectory to the robot without considering the IL muscle strength cannot ensure safety or facilitate rehabilitation. In this study, a learning-based robotic motion generation scheme was developed for RMT. The robot movement trajectories were modeled with dynamic movement primitives (DMPs), and the physical human-robot interaction was formulated as an impedance model coupled with the DMP model. To adapt the robotic motion to different pilots, reinforcement learning was used to optimize the coupled DMP model parameters. The reinforcement learning approach was implemented based on policy improvement and the path integral (PI2) algorithm, and the cost function was designed to simultaneously ensure training safety and enhance muscle strength. The proposed method was validated using a lower-extremity rehabilitation robot with magnetorheological actuators, and the experimental results demonstrated the feasibility and superiority.

Automated summary

In recent years, the number of hemiplegic patients has rapidly increased, and intelligent robots have shown great potential for rehabilitation. Robotic mirror therapy (RMT) is a promising approach that involves transferring the motion of the healthy limb to the impaired limb using a robot. However, replicating the movement trajectory of the healthy limb without considering the strength of the impaired limb can be unsafe. This study presents a learning-based approach for RMT using dynamic movement primitives (DMPs) and reinforcement learning to optimize the robot's motion.