Identification of electrically stimulated muscle after stroke

The design of controllers to enable the application of Functional Electrical Stimulation as part of a rehabilitation programme for stroke patients requires an accurate model of electrically stimulated muscle. In this paper, nonlinear dynamics of the electrically stimulated muscle under isometric conditions is investigated, leading to the requirement to identify a Hammerstein model structure. Here we develop a two-stage identification method based on a preliminary construction of the linear part that is used as an initial estimate. Then the two-stage method is applied to identify the nonlinear part and optimize the linear part. The separable least squares optimization algorithm and traditional ramp deconvolution method are implemented here and compared with the proposed method using a simulated muscle system that is based on experimental data from stroke patients. The results show that the proposed method outperforms two other previously proposed methods when implemented on the simulated muscle system with different noise levels.