Personalized Gait Patterns During Exoskeleton-Aided Training May Have Minimal Effect on User Experience. Insights from a Pilot Study

Robot-aided gait rehabilitation facilitates high-intensity and repeatable therapy. However, most exoskeletons rely on pre-recorded, non-personalized gait trajectories constrained to the sagittal plane, potentially limiting movement naturalness and user comfort. We present a data-driven gait personalization framework for an exoskeleton that supports multi-planar motion, including hip abduction/adduction and pelvic translation and rotation. Personalized trajectories to individual participants were generated using regression models trained on anthropometric, demographic, and walking speed data from a normative database. In a within-subject experiment involving ten unimpaired participants, these personalized trajectories were evaluated in regard to comfort, naturalness, and overall experience and compared against two standard patterns from the same database: one averaging all the trajectories, and one randomly selected. We did not find relevant differences across pattern conditions, despite all trajectories being executed with high accuracy thanks to a stiff position-derivative controller. We found, however, that pattern conditions in later trials were rated as more comfortable and natural than those in the first trial, suggesting that participants might have adapted to walking within the exoskeleton, regardless of the enforced gait pattern. Our findings highlight the importance of integrating subjective feedback when designing personalized gait controllers and accounting for user adaptation during experimentation.