Identification of active properties of knee joint using GA optimization

Functional Electrical Stimulation requires an accurate model of electrically stimulated muscles to control the muscle contraction force. Characterization of electrically stimulated muscle is complex because of the non-linearity and time-varying nature of the system with interdependent variables. The muscle model consists of relatively well known time-invariant passive properties and uncertain time-variant active properties. In this research a new approach for estimating nonlinear active properties of the electrically stimulated quadriceps muscle group is investigated. The objective of this study is to develop a model that could be used to describe active joint properties including continuous-time nonlinear activation dynamics and nonlinear static contraction. As an example, the modelling of a freely swinging lower leg by electrical stimulation of the quadriceps is considered.