Prediction of Human Gait Adaptations Using Optimization principles

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0302259van den BogertHumans typically perform movements in a repeatable manner, and adjust their movement to requirements of the task and to constraints imposed by the musculoskeletal system. For instance, a prosthetic limb will lead to an abnormal gait that is not a direct consequence of the device, but is rather the result of adaptations in the control of muscles in the unaffected part of the body. This research tests the hypothesis that such adaptations are governed by a general optimization principle, and can therefore be predicted. Musculoskeletal modeling and optimization will be used to predict the adaptation of human gait to ten different conditions: five speeds, two uphill slopes, two downhill slopes, and walking backwards. Theoretical predictions will be made using four optimization principles: minimal fatigue, minimal metabolic energy, minimal muscle force, minimal perceived effort. The predictions will be compared to the corresponding responses in twelve human subjects to test their validity, with expectations that the principle of minimal metabolic energy will produce the best predictions. A broader impact of this research will include its contribution to a unification of motor control, biomechanics, and bioenergetics for human locomotion. The findings will be applicable to the design of rehabilitation devices, prosthetics, surgical interventions, human-operated machines, and sport equipment. The computational techniques will be made publicly available through an interactive web interface for a target audience of high school and undergraduate students. Visitors will be able to define task conditions and optimization criterion and use the modeling engine to predict human behavior, which will be visualized using animation.