LPV subspace identification of time-variant joint impedance

Eesbeek, Stijn van; Helm, Frans van der; Verhaegen, Michel; Vlugt, Erwin de

doi:10.1109/ner.2013.6695942

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…Reflex contributions were reported to be minimal in the upper arm (Bennett et al, 1992 ) but found to be significant in the ankle (Kearney et al, 1997 ), wrist (Sinkjær and Hayashi, 1989 ), and knee (Ludvig and Perreault, 2014 ). Consequently, the results of Van Eesbeek et al ( 2013 ) cannot be directly compared to ours. Also, the range of activation is very different in the wrist compared to the ankle.…”

Section: Discussioncontrasting

confidence: 65%

“…Van Eesbeek et al ( 2013 ) also used the LPV identification to study wrist stiffness in an activation varying task. However, their method was limited to intrinsic estimates and did not decouple the effects of reflex contribution on the total torque variations.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, Sobhani Tehrani et al ( 2013a ) identified a LPV mass-spring-damper (LPV IBK) model of intrinsic ankle joint stiffness for imposed movements at rest. Soon after, Van Eesbeek et al ( 2013 ) used a LPV subspace method to identify time-variant intrinsic impedance of the human wrist joint. Subsequently, Sobhani Tehrani et al ( 2014 ) developed subspace LPV parallel-cascade (LPV-PC) method for the identification of both intrinsic and reflex stiffness during large passive ankle movements.…”

Section: Introductionmentioning

confidence: 99%

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Linear Parameter Varying Identification of Dynamic Joint Stiffness during Time-Varying Voluntary Contractions

Golkar

Tehrani

Kearney

2017

Front. Comput. Neurosci.

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Dynamic joint stiffness is a dynamic, nonlinear relationship between the position of a joint and the torque acting about it, which can be used to describe the biomechanics of the joint and associated limb(s). This paper models and quantifies changes in ankle dynamic stiffness and its individual elements, intrinsic and reflex stiffness, in healthy human subjects during isometric, time-varying (TV) contractions of the ankle plantarflexor muscles. A subspace, linear parameter varying, parallel-cascade (LPV-PC) algorithm was used to identify the model from measured input position perturbations and output torque data using voluntary torque as the LPV scheduling variable (SV). Monte-Carlo simulations demonstrated that the algorithm is accurate, precise, and robust to colored measurement noise. The algorithm was then used to examine stiffness changes associated with TV isometric contractions. The SV was estimated from the Soleus EMG using a Hammerstein model of EMG-torque dynamics identified from unperturbed trials. The LPV-PC algorithm identified (i) a non-parametric LPV impulse response function (LPV IRF) for intrinsic stiffness and (ii) a LPV-Hammerstein model for reflex stiffness consisting of a LPV static nonlinearity followed by a time-invariant state-space model of reflex dynamics. The results demonstrated that: (a) intrinsic stiffness, in particular ankle elasticity, increased significantly and monotonically with activation level; (b) the gain of the reflex pathway increased from rest to around 10–20% of subject's MVC and then declined; and (c) the reflex dynamics were second order. These findings suggest that in healthy human ankle, reflex stiffness contributes most at low muscle contraction levels, whereas, intrinsic contributions monotonically increase with activation level.

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Section: Discussioncontrasting

confidence: 65%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Linear Parameter Varying Identification of Dynamic Joint Stiffness during Time-Varying Voluntary Contractions

Golkar

Tehrani

Kearney

2017

Front. Comput. Neurosci.

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“…Recent approaches used small-window FRFs [42], wavelets [156], recursive least-squares algorithms [150], and LPV methods [135], [157]. The second goal requires perturbation techniques to estimate endpoint admittance, which do not significantly influence manual control behavior.…”

Section: A Haptic Shared Control/neuromuscular Adaptationsmentioning

confidence: 99%

Manual Control Cybernetics: State-of-the-Art and Current Trends

Mulder

Pool

Abbink

et al. 2018

IEEE Trans. Human-Mach. Syst.

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Abstract-Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This 'cybernetic approach' enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training associated with manual control. Current cybernetics theory is primarily based on technology and analysis methods formalized in the 1960s and has shown to be limited in its capability to capture the full breadth of human cognition and control. This paper reviews the current state-of-the-art in our knowledge of human manual control, points out the main fundamental limitations in cybernetics, and proposes a possible roadmap to advance the theory and its applications. Central in this roadmap will be a shift from the current linear time-invariant modeling approach that is only truly valid for human behavior under tightly controlled and stationary conditions, to methods that facilitate the analysis of adaptive, and possibly time-varying, human behavior in realistic control tasks. Examples of key current developments in the field of cybernetics -human use of preview, predictable discrete maneuvering, skill acquisition and training, time-varying human modeling, and neuromuscular system modeling -that contribute to this shift are presented in this paper. The new foundations for cybernetics that will emerge from these efforts will impact all domains that involve humans in manual and semi-automatic control.

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“…These were successfully applied in open loop conditions (Van Eesbeek et al, 2010 ; de Vlugt et al, 2012 ; de Gooijer-van de Groep et al, 2013 ). Time variant behavior of stiffness and reflexes (Ludvig et al, 2011 ; van Eesbeek et al, 2013 ; Lee and Hogan, 2014 ) can be assessed using a cascade of linear models with time-varying parameters, so called linear parameter varying (LPV) identification (Verhaegen and Verdult, 2007 ).…”

Section: Control and Plant Interactionmentioning

confidence: 99%

NeuroControl of movement: system identification approach for clinical benefit

Meskers

Groot

Vlugt

et al. 2015

Front. Integr. Neurosci.

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Progress in diagnosis and treatment of movement disorders after neurological diseases like stroke, cerebral palsy (CP), dystonia and at old age requires understanding of the altered capacity to adequately respond to physical obstacles in the environment. With posture and movement disorders, the control of muscles is hampered, resulting in aberrant force generation and improper impedance regulation. Understanding of this improper regulation not only requires the understanding of the role of the neural controller, but also attention for: (1) the interaction between the neural controller and the “plant”, comprising the biomechanical properties of the musculaskeletal system including the viscoelastic properties of the contractile (muscle) and non-contractile (connective) tissues: neuromechanics; and (2) the closed loop nature of neural controller and biomechanical system in which cause and effect interact and are hence difficult to separate. Properties of the neural controller and the biomechanical system need to be addressed synchronously by the combination of haptic robotics, (closed loop) system identification (SI), and neuro-mechanical modeling. In this paper, we argue that assessment of neuromechanics in response to well defined environmental conditions and tasks may provide for key parameters to understand posture and movement disorders in neurological diseases and for biomarkers to increase accuracy of prediction models for functional outcome and effects of intervention.

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LPV subspace identification of time-variant joint impedance

Cited by 9 publications

References 9 publications

Linear Parameter Varying Identification of Dynamic Joint Stiffness during Time-Varying Voluntary Contractions

Linear Parameter Varying Identification of Dynamic Joint Stiffness during Time-Varying Voluntary Contractions

Manual Control Cybernetics: State-of-the-Art and Current Trends

NeuroControl of movement: system identification approach for clinical benefit

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