Under normal circumstances, healthy adult humans can walk and run with a vanishingly small likelihood of falling. This level of assurance far exceeds the performance of state-of-the-art legged robots and robotic prosthetic devices. The goal of this project is to reverse engineer the dynamics and control of human locomotion using data from human subject experiments -- that is, to understand the control laws underlying human locomotion through observation of human subjects both walking naturally and responding to changes of terrain, sudden shoves, and other disturbances -- and to apply this understanding to more natural prosthetic devices and more capable legged robots. The results from the project will also give insight into movement disorders and other balance problems in the elderly and other at-risk populations, as well as allow design of more effective balance-improving devices.
In this project, the physiological control laws governing human motion will be estimated, based on experiments from both natural unperturbed walking and running, and responses to carefully chosen external perturbations during locomotion. The dynamics and the control laws near periodic motions such as walking and running will be approximated using a factorized Poincare map -- a simple generalization of the classical Poincare map. Factorized Poincare maps are inferred from experimental data using statistical techniques such as maximum likelihood estimation. The specific results this inference will allow the prediction of how the human body will return to steady locomotion in the presence of a external perturbation, in particular, it will allow the estimation of how muscle forces and body movements are modulated to recover to steady state cyclic gait. These inferred control laws will be validated using three-dimensional mathematical biped models to demonstrate quantitative prediction of movement variability and accurate estimation of the likelihood of falls.
