Development of fuzzy muscle contraction and activation model using multi-objective optimisation

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. The objective of this study is to develop an active properties model that can be implemented in biomechanical models of the lower extremities, which are generally used for the simulation of joint movements such as walking and cycling, A new approach for dynamic characterization of active properties (combination of muscle contraction and activation) of the quadriceps muscle using fuzzy model by optimizing with multi objective genetic algorithm (MOGA) is presented. MOGA is used with two objectives; to minimize the prediction error to fit the experimental data and reduce the weighting factors of the fuzzy rules to minimize the complexity of the fuzzy model. The results show that the knee joint model developed gives an accurate dynamic characterization of active properties of the knee joint.