Most research in human locomotion is limited to steady-state and constant-speed conditions. However, moving about in everyday life requires us to constantly adapt our locomotion strategies in response to intrinsic noise-like transients, uncertainties and external environmental irregularities. In this talk, I will discuss our work on understanding and predicting such adaptive locomotion behaviors in three separate studies: (1) The energetics of walking while changing speeds predicts overground walking behavior over short distances, (2) control strategies for running stably inferred from running variability stabilize running in simulation and (3) metabolic energy optimality predicts the behavior people converge to at steady-state on a split-belt treadmill. Part 2 (if there is time): Computer vision-based pose estimation for movement science Traditional sensor-based experimental measures used in movement science limit the scope and scale of the science. Recent developments in computer vision promise to transform movement science by enabling tracking of in-the-wild movement behaviors. In this talk, I will discuss how we are using such computer vision-based tools in three movement science applications: (1) providing automatic exercise feedback for individuals performing deadlifts, (2) automated infant neuromotor risk assessment using a big data approach and (3) tracking infant emotion and understanding its role in predicting neuromotor risk.