Abstract: Exoskeletons and active prostheses promise to enhance human mobility, but few have succeeded. Optimizing device characteristics on the basis of measured human performance could lead to improved designs. We have developed a method for identifying the exoskeleton assistance that minimizes human energy cost during walking. Optimized torque patterns from an exoskeleton worn on one ankle reduced metabolic energy consumption by 24.2 ± 7.4% compared to no torque. The approach was effective with exoskeletons worn on one or both ankles, during a variety of walking conditions, during running, and when optimizing muscle activity. Finding a good generic assistance pattern, customizing it to individual needs, and helping users learn to take advantage of the device all contributed to improved economy. Optimization methods with these features can substantially improve performance.

Abstract: From industrial exoskeletons to implantable medical devices, robots that interact closely with people are poised to improve every aspect of our lives. Yet designing these systems is very challenging; humans are incredibly complex and, in many cases, we respond to robotic devices in ways that cannot be modelled or predicted with sufficient accuracy. A new approach, human-in-the-loop optimization, can overcome these challenges by systematically and empirically identifying the device characteristics that result in the best objective performance for a specific user and application. This approach has enabled substantial improvements in human–robot performance in research settings and has the potential to speed development and enhance products. In this Perspective, we describe methods for applying human-in-the-loop optimization to new human–robot interaction problems, addressing each key decision in a variety of contexts. We also identify opportunities to develop new optimization techniques and answer underlying scientific questions. We anticipate that our readers will advance human-in-the-loop optimization and use it to design robotic devices that truly enhance the human experience.

Abstract: Using human responses to optimize and thus personalize assistance enhances exoskeleton performance during locomotion. Current approaches lack efficiency, comfort, rapid deployability, and computation and actuation simplicity. Here we present a method that optimizes assistance within 2 min, 16 times faster than the state-of-the-art, by effectively imitating human joint moment while ensuring stability. Optimization of a unilateral ankle exoskeleton with off-board actuation produced gentler assistance (78.2% torque) while reducing muscle activity by 36.8% and metabolic cost by 20.4% than no assistance, comparable to state-of-the-art. The method was easily and effectively deployed across new gait conditions, to bilateral devices, to knee joints and also outdoors. It largely avoided the problems of existing methods with instantaneously measurable feedback, a non-aggressive tuning process, a reasonable tuning direction, and a non-parametric assistance formulation. By significantly reducing pre-research, operational, user physiological and psychological costs, this method largely elevates the accessibility level of effective, personalized and continuously tuned exoskeletons in everyday scenarios.