a b s t r a c tDespite significant recent interest in the identification of electrically stimulated muscle models, current methods are based on underlying models and identification techniques that make them unsuitable for use with subjects who have incomplete paralysis. One consequence of this is that very few model-based controllers have been used in clinical trials. Motivated by one case where a model-based controller has been applied to the upper limb of stroke patients, and the modelling limitations that were encountered, this paper first undertakes a review of existing modelling techniques with particular emphasis on their limitations. A Hammerstein structure, already known in this area, is then selected, and a suitable identification procedure and set of excitation inputs are developed to address these short-comings. The technique that is proposed to obtain the model parameters from measured data is a combination of two iterative schemes: the first of these has rapid convergence and is based on alternating least squares, and the second is a more complex method to further improve accuracy. Finally, experimental results are used to assess the efficacy of the overall approach.
a b s t r a c tModeling of electrically stimulated muscle is considered in this paper where a Hammerstein structure is selected to represent the isometric response. Motivated by the slowly time-varying properties of the muscle system, recursive identification of Hammerstein structures is investigated. A recursive algorithm is then developed to address limitations in the approaches currently available. The linear and nonlinear parameters are separated and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. Hence the procedure is termed the alternately recursive least square (ARLS) algorithm. When compared with the leading approach in this application area, ARLS exhibits superior performance in both numerical simulations and experimental tests with electrically stimulated muscle.
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.
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