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Recent advances in wearable robotics have unveiled the potential of exoskeletons to augment human locomotion across a variety of environments. However, few studies have evaluated the capability of these devices to augment human balance during unstable locomotion. In this talk, I will discuss how we’re threading together biomechanics, mechanical and mechatronic design, wearable sensor-informed machine learning, and controls to work towards a balance-augmenting exoskeleton. I’ll start by discussing human balance, recovery strategies, and some of the most challenging destabilizing scenarios that we have identified through our recent studies. I will also discuss how we are utilizing machine learning to estimate and predict human biomechanical outcomes in these destabilizing environments. Lastly, I will introduce our ongoing work to integrate biomechanics and machine learning to develop intelligent exoskeleton control algorithms for balance augmentation.
Jennifer Leestma (Georgia Institute of Technology )
Jennifer Leestma is a Ph.D. Candidate in Robotics at the Georgia Institute of Technology in Atlanta, GA. At Georgia Tech, Jenny works in the Exoskeleton & Prosthetic Intelligent Controls (EPIC) Lab and the Physiology of Wearable Robotics (PoWeR) Lab under the supervision of Dr. Aaron Young and Dr. Greg Sawicki. Her work focuses on studying human biomechanical stability during locomotion and balance augmentation using machine-learning driven control methods for robotic lower limb exoskeletons. Before Georgia Tech, Jenny worked in various research groups that focused on prosthetic limbs and rehabilitation engineering at the Minneapolis VA Hospital, the University of Wisconsin-Madison, the Shirley Ryan AbilityLab, and Össur. She received her M.S. in Mechanical Engineering from Georgia Tech and her B.S. in Biomedical Engineering from the University of Wisconsin-Madison. Jenny’s doctoral work has been supported by the National Science Foundation through a Graduate Research Fellowship (NSF GRFP) and National Research Traineeship (NSF NRT ARMS).