Traditional lower-limb prosthetic devices do not provide net power over a gait, and therefore limit the locomotion of amputees. Powered prostheses devices have the potential of providing natural locomotion to amputees;however, there is a gap in our knowledge as to how these powered devices should be controlled. In this study we propose to develop a parameter identification technique applicable to human locomotion control modeling. We will use mathematical models to capture the complex dynamics of human locomotion, including ground impacts. Subsequently, we will develop a parameter identification technique based on the method of Inverse Optimal Control (IOC). IOC allows us to compute the cost function that best explains the observed behavior of a controlled dynamical system. Applied to observations of human locomotion (e.g., motion capture data), this method will produce a cost function, which in turn will produce an optimal choice of parameters for the prosthesis. This method can therefore automate the lengthy process of parameter tuning, and significantly reduce the number and duration of clinical visits for lower-limb amputees.
The objective of this project is to develop a parameter identification technique applicable to human locomotion control modeling. This method will automate the lengthy process of parameter tuning for control of lower-limb above-knee prostheses, and will therefore have clinical significance.