Exoskeleton Gait Adaptation Framework via Hm-DMP and PI2 Optimization for Dynamic Patient Mobility Matching

Abstract

Repetitive gait training with lower-limb exoskeletons enhances neuroplasticity and reduces muscle atrophy by promoting patient engagement in active rehabilitation training. Importantly, the therapeutic efficacy of such engagement critically depends on providing patients with task difficulty levels matching their real-time walking capacities. To address this, a closed-loop Mobility-Matching Framework is proposed, integrating Hybrid Multi-attractor Dynamic Movement Primitives (Hm-DMP) with Policy Improvement with Path Integral (PI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) optimization, which achieves real-time trajectory adaptation. The Hm-DMP module preserves critical kinematic invariants of normative gait patterns during trajectory deformation through constrained multi-attractor modulation. Simultaneously, the PI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-driven optimizer iteratively adjusts joint trajectory keypoints of Hm-DMP by optimizing a hybrid cost function, enabling dynamic matching between training trajectories and patients’ real-time mobility. Experimental trials on the WEI-EXO platform demonstrate the proposed framework’s robustness to detect and respond to real-time changes in patient’ ambulatory capacity by optimizing assistance trajectories while preserving the normative gait kinematics. This closed-loop adaptation process facilitates personalized gait rehabilitation with exoskeletons, enhancing training efficacy and maintaining comfort across patients with diverse mobility levels.