Aaron Young (Associate Professor and Faculty Fellow at Georgia Tech)

New end-to-end task agnostic AI systems have transformed the ability of wearable robotics to provide meaningful assistance to users across a diverse range of daily activities. This talk will present best practices for structuring real-time deep learning systems for wearable robotics that integrate seamlessly with the user’s underlying physiology. Outcome measures from related human subject experiments with test scenarios of these systems will be presented. Building on this, the emerging science of transfer learning has transformed our ability to deploy large-scale deep learning models at scale across diverse sets of wearable robots, tasks, and populations, which removes the need for new research teams and companies to collect numerous amounts of expensive, labeled data to train these systems. Our group’s latest advances towards leveraging transfer learning to create foundational human motion models will be discussed. Lastly, these techniques can be applied to a very broad set of practical problems, and two unique deployment scenarios will be discussed, one clinical and one non-clinical application. Our research team recently completed a clinical trial in mobility impaired individuals from stroke using hip exosuits to enhance daily activities of living. Second, strenuous industrial tasking can lead to injuries which our team has found indications that these can be mitigated through effective clothing-integrated robotic systems that provide task agnostic assistance.

Bio:

Aaron Young is an Associate Professor and Woodruff and James R. and Sarah R. Borders Faculty Fellow in the Woodruff School of Mechanical Engineering at Georgia Tech and has directed the Exoskeleton and Prosthetic Intelligent Controls (EPIC) lab since 2016. Dr. Young received his MS and PhD degrees in Biomedical Engineering with a focus on neural and rehabilitation engineering from Northwestern University in 2011 and 2014 respectively. He received a BS degree in Biomedical Engineering from Purdue University in 2009. He also completed a post-doctoral fellowship at the University of Michigan in the Human Neuromechanics Lab working with lower limb exoskeletons and powered orthoses to augment human performance. His research area is in advanced control systems for robotic prosthetic and exoskeleton systems for humans with movement impairment. He combines machine learning, robotics, human biomechanics, and control systems to design wearable robots to improve the community mobility of individuals with walking disability. He has received an NIH New Innovator Award (DP2), NIH NCMRR New Investigator award and IEEE New Faces of Engineering award, and his EPIC lab group won the International VIP Consortium Innovation Competition. He is a Senior Member of the IEEE and National Academy of Inventors (NAI).