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

2025-12-19

AI Summary

Conventional exoskeleton-based rehabilitation devices employ non-personalized, sagittal-plane-restricted pre-recorded gait trajectories, limiting movement naturalness and user comfort. Method: This study presents the first systematic evaluation of personalized multi-planar gait trajectories in an exoskeleton: subject-specific 3D hip and pelvis trajectories were generated via regression models incorporating anthropometric, demographic, and walking-speed data; a within-subject experimental design with a stiff position-derivative controller was conducted on ten healthy participants. Contribution/Results: Trajectory personalization alone did not significantly improve subjective comfort or naturalness. Instead, users’ rapid adaptation to the device emerged as the dominant factor influencing subjective experience. These findings reveal a paradigm shift—“adaptability outweighs personalization”—in human-exoskeleton co-adaptation, challenging prevailing assumptions in rehabilitation robotics. The results provide empirical evidence for optimizing clinical rehabilitation strategies by prioritizing adaptive training protocols over trajectory customization.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Intelligent Robots: Motion and Path Planning
• Humans and AI: User Experience and Usability

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

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.

Problem

• Evaluating personalized gait patterns in exoskeleton training
• Comparing user experience across personalized and standard gait trajectories
• Investigating adaptation effects on comfort and naturalness in exoskeleton use

Innovation

• Data-driven framework personalizes multi-planar exoskeleton gait trajectories
• Regression models generate personalized patterns from anthropometric and demographic data
• High-accuracy stiff position-derivative controller executes personalized trajectories