Experimental Identification of Skeletal Muscle Biomechanical Parameters with Sigma Point Kalman Filter

This work establishes a computational identification of skeletal muscle biomechanical parameters based on experiments. We previously proposed a mathematical muscle model which describes the complex physiological system of skeletal muscle based on the macroscopic Hill and microscopic Huxley concepts. The original skeletal muscle model enables consideration for the muscular masses and the viscous frictions by muscle tendon complex. In this paper, we present an experimental identification method of biomechanical parameters with Sigma-Point Kalman Filter under nonlinear differential equations of our model. SPKF has higher accuracy and consistency for nonlinear estimation than extended kalman filter. Result of the estimation shows its effective performance.