Comparison of closed-loop system identification techniques to quantify multi-joint human balance control

Engelhart, Denise; Boonstra, Tjitske; Aarts, RM Ronald; Schouten, Alfred C.; Kooij, Herman van der

doi:10.1016/j.arcontrol.2016.04.010

Cited by 20 publications

(14 citation statements)

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“…Computational postural control models help us to precisely decode each facet of postural instability in a quantitative manner [3]; and to bind neurophysiological bases to quantitative biomarkers [17]. There have been few attempts to understand PD patients’ instability by postural control models [13, 18, 19].…”

Section: Introductionmentioning

confidence: 99%

Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model

Rahmati

Schouten

Behzadipour

et al. 2019

J NeuroEngineering Rehabil

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Background Impaired postural control in Parkinson’s disease (PD) seriously compromises life quality. Although balance training improves mobility and postural stability, lack of quantitative studies on the neurophysiological mechanisms of balance training in PD impedes the development of patient-specific therapies. We evaluated the effects of a balance-training program using functional balance and mobility tests, posturography, and a postural control model. Methods Center-of-pressure (COP) data of 40 PD patients before and after a 12-session balance-training program, and 20 healthy control subjects were recorded in four conditions with two tasks on a rigid surface (R-tasks) and two on foam. A postural control model was fitted to describe the posturography data. The model comprises a neuromuscular controller, a time delay, and a gain scaling the internal disturbance torque. Results Patients’ axial rigidity before training resulted in slower COP velocity in R-tasks; which was reflected as lower internal torque gain. Furthermore, patients exhibited poor stability on foam, remarked by abnormal higher sway amplitude. Lower control parameters as well as higher time delay were responsible for patients’ abnormal high sway amplitude. Balance training improved all clinical scores on functional balance and mobility. Consistently, improved ‘flexibility’ appeared as enhanced sway velocity (increased internal torque gain). Balance training also helped patients to develop the ‘stability degree’ (increase control parameters), and to respond more quickly in unstable condition of stance on foam. Conclusions Projection of the common posturography measures on a postural control model provided a quantitative framework for unraveling the neurophysiological factors and different recovery mechanisms in impaired postural control in PD. Electronic supplementary material The online version of this article (10.1186/s12984-019-0574-0) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model

Rahmati

Schouten

Behzadipour

et al. 2019

J NeuroEngineering Rehabil

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“…As técnicas de identificação de sistemas em combinação com distúrbios externos especificamente projetados fornecem uma maneira de desembaraçar causa e efeito no controle do equilíbrio e identificar a dinâmica do controlador neuromuscular [14]. Como o sistema de controle de equilíbrio é dinâmico (isto é, sua resposta é descrita como uma função do tempo), as técnicas de identificação do sistema podem ser usadas para determinar as estruturas subjacentes do sistema, desenrolando relações de causa e efeito na coordenação multi-articular.…”

Section: Figura 1: Modelo Sistema De Manutenção De Equilíbriounclassified

“…As técnicas de identificação de sistemas em combinação com aplicação de perturbações externas ao indivíduo, especificamente projetadas, fornecem uma maneira de desembaraçar causa e efeito no controle do equilíbrio, de identificar a dinâmica do controlador neuromuscular e fornecer uma maneira de detectar deficiências dos mecanismos subjacentes [14]. Desta forma, as técnicas de identificação de sistemas podem ser usadas para estimar o modelo matemático do sistema de manutenção do equilíbrio [15] [16,17] [12].…”

Section: Introductionunclassified

Modelagem Matemática Do Sistema De Manutenção De Equilíbrio Empregando Técnicas De Identificação De Sistemas

Helmann.¹,

A²,

Meneghel.³

2018

Anais Do v Congresso Brasileiro De Eletromiografia E Cinesiologia E X Simpósio De Engenharia Biomédica

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Resumo: O controle postural é essencial para o desempenho das atividades da vida diária, mas seu desempenho diminui com o envelhecimento ou doenças, aumentando o risco a queda. Os testes atuais de equilíbrio clínico são incapazes de quantificar esses mecanismos subjacente. A modelagem matemática aliada à simulação computacional apresenta uma forma de reproduzir o comportamento do sistema de manutenção de equilíbrio humano e propor soluções para prevenção de quedas. As técnicas de identificação de sistemas em combinação com perturbações externas podem fornecer uma maneira de detectar deficiências dos mecanismos subjacentes e estimar um modelo não paramétrico do sistema de manutenção de equilíbrio. Palavras-chave: Modelagem matemática, sistema de manutenção de equilíbrio, técnicas de identificação de sistemas.Abstract: Postural control is essential for the performance of activities of daily living, but its performance decreases with aging or diseases, increasing the risk of falls. Current clinical balance tests are unable to quantify these underlying mechanisms. The mathematical modeling allied to the computational simulation presents a way to reproduce the behavior of the human balance maintenance system and propose solutions to prevent falls. Systems identification techniques in combination with external perturbations can provide a way to detect deficiencies of the underlying mechanisms and to estimate a nonparametric model of the equilibrium maintenance system. Keywords: Mathematical modeling, equilibrium maintenance system, systems identification techniques. IntroduçãoO grande interesse no sistema de manutenção de equilíbrio humano resultou em muitas publicações em que o objetivo comum era identificar os mecanismos de controle do equilíbrio. O sistema de manutenção do equilíbrio envolve o sistema nervoso central, o sistema sensorial e sistema músculo-esquelético. O sistema músculo-esquelético é o responsável pelo controle postural, seja em situações (quase) estáticas seja em situação de movimentação dinâmica. Este sistema garante o controle postural através da integração de sinais oriundos do sistema sensorial para determinar a resposta apropriada, que é enviada aos músculos e resulta em torques articulares corretivos para manter o corpo na posição desejada [1]. O controle postural é essencial para o desempenho das atividades da vida diária, mas seu desempenho diminui com o envelhecimento [2]. O envelhecimento degrada a capacidade do sistema de manutenção do equilíbrio, aumentando a possibilidade da incidência de quedas.[3].Uma queda pode gerar consequências graves, como fraturas, hospitalização, perda da independência, maior susceptibilidade a uma futura queda com risco de mortalidade, medo de cair, limitação das atividades e aumento do risco de institucionalização [4]. As lesões relacionadas com quedas constituem dois terços de todas as lesões não intencionais que levam à morte em idosos. Em torno de 30 a 40% dos idosos que sofrem quedas, morrem cada ano [5]. Além disso, sabe-se que o risco de queda com o en...

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“…SI is a significant research area in the field of control and modeling due to its ability to represent and quantify variable interactions in complex systems. Several applications of SI in literature are for understanding of complex natural phenomena [7]- [11], model-based design of control engineering applications [12]- [16] and project monitoring and planning [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

Binary Particle Swarm Optimization Structure Selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) Model of a Flexible Robot Arm

Yassin¹,

Zabidi²,

Ali³

et al. 2016

International Journal on Advanced Science, Engineering and Information Technology

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The Nonlinear Auto-Regressive Moving Average with Exogenous Inputs (NARMAX) model is a powerful, efficient and unified representation of a variety of nonlinear models. The model's construction involves structure selection and parameter estimation, which can be simultaneously performed using the established Orthogonal Least Squares (OLS) algorithm. However, several criticisms have been directed towards OLS for its tendency to select excessive or sub-optimal terms leading to nonparsimonious models. This paper proposes the application of the Binary Particle Swarm Optimization (BPSO) algorithm for structure selection of NARMAX models. The selection process searches for the optimal structure using binary bits to accept or reject the terms to form the reduced regressor matrix. Construction of the model is done by first estimating the NARX model, then continues with the estimation of the MA model based on the residuals produced by NARX. One Step Ahead (OSA) prediction, Mean Squared Error (MSE) and residual histogram analysis were performed to validate the model. The proposed optimization algorithm was tested on the Flexible Robot Arm (FRA) dataset. Results show the success of BPSO structure selection for NARMAX when applied to the FRA dataset. The final NARMAX model combines the NARX and MA models to produce a model with improved predictive ability compared to the NARX model.

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Comparison of closed-loop system identification techniques to quantify multi-joint human balance control

Cited by 20 publications

References 56 publications

Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model

Disentangling stability and flexibility degrees in Parkinson’s disease using a computational postural control model

Modelagem Matemática Do Sistema De Manutenção De Equilíbrio Empregando Técnicas De Identificação De Sistemas

Binary Particle Swarm Optimization Structure Selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) Model of a Flexible Robot Arm

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