Data for: Quantifying human adaptation to a novel split-belt walking condition after broad experience at different belt speeds

This dataset includes raw MATLAB files generated by the Qualisys Track
Manager software from motion capture data of 15 non-disabled participants
walking on a split-belt treadmill, with only ankle markers. It also
contains metabolics measured from the COSMED K5 metabolic cart during
treadmill walking. The dataset is associated with the article
"Quantifying human adaptation to a novel split-belt walking condition
after broad experience at different belt speeds". DOI will be updated
shortly after official submission. Article abstract: Humans can adapt
their gait to minimize energy cost when given sufficient exposure to novel
energy landscapes. However, it remains unclear whether broad experience in
one energy landscape is sufficient to initiate continuous optimization
when encountering similar but distinct conditions. In this study, we used
visual biofeedback to guide 15 participants to broadly explore walking
gaits with different step length asymmetries (SLAs) while walking on a
split-belt treadmill with a large split-belt ratio. We subsequently tested
how participants adapted their walking patterns during free exploration
trials at the large split-belt ratio and at a new, smaller split-belt
ratio. Our results showed that during guided exploration, participants
were exposed to energetically favorable conditions. However, participants
did not self-select walking patterns that minimized their metabolic cost
during either free exploration trial. When first exposed to the smaller
split-belt ratio, participants immediately adjusted their leg swing
distances and belt contact times while maintaining similar step lengths.
Throughout this trial, they continued adapting by significantly increasing
step lengths on the fast belt. Taken together, our results suggest that
participants actively modified their gait strategy when exposed to an
energy landscape distinct from the one in which they gained broad
experience.