66 research outputs found
Identification of electrically stimulated muscle after stroke
No full textStroke affects a large percentage of the population in UK and one of the most devastating and common consequences of the stroke is loss of the use of the arm and hand. Currently there is increasing interest in the application of control schemes as part of a rehabilitation programme for survivors of a stroke. Functional Electrical Stimulation is applied, together with the model-based controller in order to ensure that the assistance provided coincides as much as possible with the patient’s voluntary intention. The difficulty encountered is lack of a reliable model of electrically stimulated muscle. Motivated by this, this thesis focus on identification of electrically stimulated muscle, especially the impaired arm after stroke.After studying the muscle behaviors and reviewing the existing muscle models, Hammerstein structure is chosen to model the nonlinear dynamics of the electrically stimulated muscle under isometric conditions. Firstly, batch identification algorithms are considered. A two-stage algorithm is proposed, together with its identification procedure and comparison results on a stimulated muscle system. Due to its simple implementation and good performance, this algorithm has been developed to the later two iterative algorithms. Experimental results are used to demonstrate the superior performance of the algorithms and the model structure when compared with others.Further more, considering the slowly time-varying properties of the muscle system, recursive identification of Hammerstein structure is investigated later in the thesis. A novel recursive identification algorithm is developed, where 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. When compared with the leading technique involving over-parametrization together with a Recursive Least Squares algorithm on numerical examples and experimental data, the proposed algorithm exhibits superior performance.Finally, the identified muscle models have been used in FES control schemes for electrically stimulated muscle under isometric conditions and iterative learning controllers will be used since the repeated nature of the task. Besides the two nonlinear ILC approaches, several trial-dependent and adaptive control schemes has been designed and implemented in the thesi
Identification of Electrically Stimulated Muscle after Stroke
No full textThe 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
Online Identification of Electrically Stimulated Muscle Models
No full textOnline identification of electrically stimulated muscle under isometric conditions, modeled as a Hammerstein structure, is investigated in this paper. Motivated by the significant time-varying properties of muscle, a novel recursive algorithm for Hammerstein structure is developed. 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 Recursive Least Squares (RLS) algorithm applied to the over-parametric representations of the Hammerstein structure, ARLS exhibits superior performance on experimental data from electrically stimulated muscles and a faster computational time for a single updating step. Performance is further augmented through use of two separate forgetting factors
Recursive Identification of Hammerstein Systems with application to Electrically Stimulated Muscle
No full textTwo methods for recursive identification of Hammerstein systems are considered. In the first method, the recursive least squares algorithm is applied to an overparameterized representation of the Hammerstein model and a rank-1 approximation is used to recover the linear and nonlinear parameters from the estimated overparameterized form. In the second method, the linear and nonlinear parameters are recursively estimated in an alternate manner. The superiority of the second method is confirmed using a numerical simulation example, together with experimentally measured data from electrically stimulated muscles
System Identification of Muscle Dynamics for ILC-based Stroke Rehabilitation
No full textIn this paper results are presented for the identification of electrically stimulated muscle dynamics in stroke patients. This research forms a critical component in the model-based control of electrically stimulated upper-limb movement, which, in turn, is necessary to maximise improvement in sensory-motor function during rehabilitation with electrical stimulation. An overview is firstly provided of an experimental test facility that has been developed for stroke rehabilitation. During treatment stroke participants use this system to track elliptical trajectories projected onto a target above their arm, assisted by electrical stimulation applied to their triceps. The control approach used to apply stimulation is summarised, and the structure of the muscle model within the scheme is described. A novel iterative identification scheme is then introduced for the muscle dynamics of stroke patients, and experimental results are presented to confirm its performance and suitability in the proposed rehabilitation context
Recursive Identification of Hammerstein Structure
No full textA novel recursive algorithm for identification of Hammerstein structures is developed. The linear and nonlinear parameters are separated and estimated recursively in a parallel manner, but each updating algorithm employs the estimation produced by the other at the previous time instant. Hence, it is termed the Alternately Recursive Least Square (ARLS) algorithm. When compared with Recursive Least Squares (RLS) algorithm applied to the over-parametric representations of the Hammerstein structure, ARLS demonstrated superior performance over extensive numerical simulations
Identification of electrically stimulated muscle models of stroke patients
No full textDespite 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 modeling limitations that were encountered, this paper first undertakes a review of existing modeling 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
Identification of the Dynamics of Human Arms after Stroke
No full textThe 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 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 the other one when implemented on the simulated muscle system with different noise levels
Possible Involvement of Multidrug-Resistant Hepatitis B Virus sW172*Truncation Variant in the ER Stress Signaling Pathway during Hepatocarcinogenesis
No full textWe investigated the biological effect of hepatitis B virus (HBV) rtA181T/sW172* point mutation on HBsAg secretion and the potential mechanisms involved in hepatocarcinogenesis. Fulllength HBV wild type (wt) and HBV rtA181T/sW172* expression plasmids were transfected into HepG2 cell lines or were injected into C57BL/6 mice. The extracellular and intracellular expression levels of HBsAg and HBeAg proteins, in mouse serum and liver tissues were detected by ELISA. The localization of the truncated protein was characterized in vitro. The mRNA expression of endoplasmic reticulum (ER) stress gene GRP78 was determined. HBsAg levels were significantly higher in both supernatant of cells transfected with HBV wt and serum of mice injected with HBV wt, compared with that of HBV rtA181T/sW172* mutant. The reversed trend was observed in intracellular cells and intrahepatic liver cells. Wild type S protein alone could rescue this dysfunction. HBV rtA181T/sW172* truncated surface proteins showed a more aggregated cytoplasmic pattern which were also localized to the ER in comparison with HBV wt. Furthermore, GRP78 mRNA expression was increased 72 h post-transfection in HBV rtA181T/sW172* cells relative to HBV wt cells (P = 0.0154). The HBV sW172* truncation variant has a defect on HBsAg secretion which can lead to surface protein retention in the ER, where it may contribute to hepatocarcinogenesis through activating the ER stress signaling pathway.National Natural Science Foundation of China [81400607]SCI(E)[email protected]
The Inverse Problem for Elliptic Equations from Dirichlet to Neumann Map in Multiply Connected Domains
No full textThe present paper deals with the inverse problem for linear elliptic equations of second order from Dirichlet to Neumann map in multiply connected domains. Firstly the formulation and the complex form of the problem for the equations are given, and then the existence and global uniqueness of solutions for the above problem are proved by the complex analytic method, where we absorb the advantage of the methods in previous works and give some improvement and development. Copyright (C) 2009 Guochun Wen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Mathematics, AppliedMathematicsSCI(E)0ARTICLEnul
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