Author: Abram, Sabrina
Humans can learn to move optimally. For many movements, we have a control strategy—or control policy—that optimizes some objective. In walking, we prefer the combination of step widths, step lengths, and speeds that optimizes the amount of energy we need. In familiar contexts, we have had many opportunities to establish this optimal control policy. But in new contexts, the nervous system must quickly learn new control policies in order to continue to move optimally. Our lab has recently demonstrated that humans can continuously optimize energetic cost during walking. This is an impressive feat given that the nervous system has tens of thousands of motor units at its disposal, and it can coordinate these motor units over millisecond timescales, which results in countless combinations of motor unit coordination. The goal of this thesis is to determine how the nervous system navigates this combinatorial problem to learn new energy optimal control policies in new walking contexts. I used three distinct studies to accomplish this goal. For the first two studies, I designed and implemented a simple mechatronic system that applies energetic penalties in the form of walking incline as a function of gait. This creates a new relationship between gait and energetic cost—or new cost landscape—that shifts the energy optimal gait. For the third study, I used exoskeletons that apply assistive torques to each ankle at each walking step to shift the energy optimal gait. The first study tested whether previous findings that people can learn to adapt their control policy when the energy optimum is shifted along step frequency generalize to a different gait parameter and to a different experimental setup. I found that, like step frequency, people can learn to adapt their control policy when the energy optimum is shifted along step width. The second study tested if and how energy optimization extends to multiple gait parameters at the same time. I found that, when the energy optimum is shifted along step width and step frequency, people are limited in their ability to optimize both gait parameters. The third study asked how people learn in which ways to optimize their policy. I found that general variability leads to specific adaptation toward optimal policies. Taken together, these findings provide insight into the mechanisms that underlie energy optimization in walking, as well as the limitations of this optimization.
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Thesis advisor: Donelan, J. Maxwell
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