Optimized Mappings From Biological Hip Moment Estimates to Exoskeleton Torque Can Personalize Assistance Across Users and Generalize Across Tasks

Abstract

Recent advancements in data-driven methods have enabled real-time estimation of biomechanical states for exoskeleton control. While biological joint moments can be directly used to scale exoskeleton assistance, this approach is often suboptimal. An optimized mapping between biological joint moments and exoskeleton assistance could enhance end-to-end controllers based on the user’s physiological state. We introduce a flexible parametrization of biological moment-based control using delay, scaling, and shaping terms to transform joint moment estimates into commanded torque. We performed human-in-the-loop optimization, using metabolic cost to evaluate each iteration’s controller parameters, for 9 subjects across three ambulation modes: level walking at 1.1 m/s, 1.5 m/s, and 5° inclined walking. We evaluated three methods of exoskeleton control: 1. Personalized/Task Dependent, 2. Theoretical Task Dependent/Non-personalized, and 3. Theoretical Multi-Task/Non-personalized. On average, our personalized approach provided the greatest benefit of 18.3% reduction in metabolic cost compared to walking without the exoskeleton, with the task-dependent and multi-task controllers producing similar reductions of 8.6% and 8.4%, respectively. Our results show that fully personalized exoskeleton control parameters can yield large metabolic reductions for cyclic ambulatory tasks when compared to a theoretical generalizable, multi-task controller, highlighting the value of personalizing exoskeleton assistance to users across ambulatory tasks.