The control and assessment of modern exoskeletons are two factors that greatly hinder the translation of these exciting technologies. To assist their wearer, exoskeletons employ control strategies that govern their assistance. The tuning of controller parameters that govern exoskeleton assistance is a laborious process that can require specialized researchers or equipment. In this talk, I will describe our approach to controller tuning that leverages user preference as an effective, expedient, and convenient tuning methodology. I will discuss results of a recent experiment where we quantified user preferred controller settings and their selection consistency. We allowed subjects to blindly search for their preferred controller settings using a two-dimensional touchpad while wearing bilateral ankle exoskeletons. We quantified how user preference and consistency varied across speeds, sessions, and experience level. Secondly, I will discuss a recent study where we quantified the human perceptual ability to perceive changes in their metabolic rate during exoskeleton assisted locomotion. Metabolic rate is a common metric of success for exoskeleton technologies, but the degree to which humans can perceive these changes is unknown, and important for decision making and adoption. In this study, participants experienced a metabolic rate that resulted from ankle exoskeleton assistance. The participant’s metabolic rates were manipulated by carefully shifting the assistance provided by the exoskeleton. Participants responded to whether they experienced an increased or decreased metabolic rate for different assistance settings; these data were aggregated to determine their perceptual threshold. I will show that the perception threshold for metabolic rate is greater than what is currently achievable with modern exoskeletons. Finally, I will close by highlighting new work that investigates the value added by lower-limb exoskeletons using tools from behavioral economics.
Elliott Rouse is an Assistant Professor in the Department of Mechanical Engineering and a Core Faculty Member in the Robotics Institute at the University of Michigan. He directs the Neurobionics Lab, whose vision is to reverse engineer how the nervous system regulates the mechanics of locomotion, and use this information to develop better wearable robotic technologies. He is the recipient of the NSF CAREER Award and is a member of the IEEE EMBS Technical Committee on Biorobotics. In addition, he is on the Editorial Boards for IEEE Robotics and Automation Letters, IEEE Transactions on Biomedical Engineering, and Wearable Technologies. Elliott received the BS degree in mechanical engineering from The Ohio State University and the PhD degree in biomedical engineering from Northwestern University. Subsequently, he joined the Massachusetts Institute of Technology as a Postdoctoral Fellow in the MIT Media Lab. In 2019 – 2020, Elliott was a visiting faculty member at (Google) X in California where he maintains an appointment. Elliott and his research have been featured at TED, on the Discovery Channel, CNN, Digital Trends, Business Insider, among others.
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