Human-Exoskeleton Locomotion Interaction Experience Transfer: Speeding up and Improving the Performance of Preference-Based Optimizations of Exoskeleton Assistance During Walking

Preference-based optimizing methods have shown their advantages and potential in exploring individual, com- fortable, and effective control strategies and assistance param- eters of exoskeletons during locomotion. Research indicates that compared with naive wearers, knowledgeable wearers with abundant exoskeleton assistance experience have obvious advantages in speeding up the parameters exploration process and improving the assistant performance. However, there is no existing method that could utilize the human-exoskeleton locomotion interaction experience (HELIE) to assist naive wearers during the exploration process. In this work, we pro- pose a novel preference-based human-exoskeleton locomotion interaction experience transfer (LIET) framework, which could speed up the exploration of human-preferred parameters and acquire more satisfying results for naive wearers via the HELIE acquired from knowledgeable wearers. In addition, based on the proposed LIET framework, we establish the mathematical expression of the HELIE transfer during exoskeleton assistance. This will promote the research that concerns utilizing HELIE for exoskeleton control parameters optimizations in the future. Finally, experiments demonstrate the proposed LIET frame- work could speed up the exploration process and acquire more satisfying optimized results for naive wearers.