Challenge-Based Adaptation of Exoskeleton Assistance and Gamified Biofeedback Enables Automated Gait Rehabilitation

Abstract

Robotic and biofeedback-assisted interventions are promising alternatives to surgical intervention and supplements for traditional physical therapy for children with gait impairments. This work utilizes a human-in-the-loop optimization strategy to adaptively modulate parameters for a lightweight robotic knee exoskeleton and biofeedback video game to maximize learning potential following the challenge point framework. We tested our approach on three able-bodied participants and one pediatric patient with genu recurvatum, a common walking pattern in children with neurological injuries. We implement a Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) optimizer to enforce a target success rate of 70 % by continuously adjusting visual biofeedback and exoskeleton assistance parameters. Our experimental results demonstrate the system's ability to maintain the target challenge level for the pediatric participant. Stance hyperextension decreased significantly from pre- to post-training trials on day $2\left(9.2^{\circ}\right)$ and $3\left(3.2^{\circ}\right)$ of the case study. Swing flexion approached the clinical target of 65° by the end of the third day. The promising optimizer performance and changes in gait kinematics validate the feasibility of autonomous parameter tuning to maximize learning potential in pediatric gait rehabilitation.