AEM Seminar:
Developing Generalizable Model-Based Control Methods for Lower-Limb Prostheses
Abstract:Lower-limb
powered prostheses hold potential to improve quality of life for
600,000 people with lower-limb amputations by enabling mobility with
less human energy than traditional prostheses. However, current
prosthesis control strategies require many hours of heuristic tuning for
every user and for every behavior. To develop a control approach that
applies across users, we construct model-based prosthesis control
methods that rely solely on local information by translating formal
nonlinear bipedal walking control methods to prostheses. On hardware, we
realized the first model-dependent prosthesis controller that utilizes
real-time force sensing to complete the model, achieving improved
tracking performance across terrains and subjects without additional
tuning. Going forward, we will examine how to incorporate biomechanics
principles into these model-based formulations to recover natural
behavior and improve user
outcomes.
Bio:Dr. Rachel
Gehlhar Humann is an Assistant Professor in Mechanical Engineering at
the University of Minnesota. Here she leads the Humann Bionics Lab,
which seeks to improve generalizability of lower-limb powered prosthesis
control methods through model-based control strategies that incorporate
human sensor feedback and biomechanics principles. Prior to joining UMN
in 2024, she conducted her postdoctoral research at UCLA, exploring the
effects of ankle stiffness on walking gait. She completed her M.S.
(2018) and Ph.D. (2022) in Mechanical Engineering at the California
Institute of Technology where she investigated nonlinear control methods
for powered prostheses. Before Caltech, she graduated with a B.S. in
mechanical engineering from the University of St. Thomas in St. Paul, MN
in 2016.