Assessing Standing Balance using MIMO Closed Loop System Identification Techniques

Pasma, Jantsje H.; Engelhart, Denise; Maier, Andrea B.; Meskers, Carel G.M.; Aarts, RM Ronald; Schouten, Alfred C.; Kooij, Herman van der

doi:10.1016/j.ifacol.2015.12.325

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…The obtained Q and R were then used to calculate LQR gains according to equations ( 24) and ( 25) below. Algebraic Riccati equation (equation (2)) is solved to obtain S based on Q and R. The LQR gain vector (k) is obtained based on equation (25) (see figure 3). Note, equation (24) has multiple answers; however, the answer that makes the closed-loop system stable via k is selected.…”

Section: Neural Control Identificationmentioning

confidence: 99%

“…Previous studies proposed that the neural control generates motor commands based on task goals of minimizing the angular position and velocity [1, 2, 23,24] as well as acceleration [1, 2] with respect to the static upright posture. Many studies have provided non-parametric estimates of the neural dynamics in both standing and sitting postures using linear closed-loop system identification [1,2,10,25]. Other studies have provided parametric estimates of the neural dynamics by using linear controllers such as proportional-integral-derivative (PID) control [10,21,26,27], proportional-derivative (PD) control [22,23], and PD control with acceleration feedback [1,28] to model the neural control functioning for controlling postural stability.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear neural control using feedback linearization explains task goals of central nervous system for trunk stability in sitting posture

Vette

Noamani

2023

J. Neural Eng.

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Objective: Characterizing the task goals of the neural control system for achieving seated stability has been a fundamental challenge in human motor control research. This study aimed to experimentally identify the task goals of the neural control system for seated stability.Approach: Ten able-bodied young individuals participated in our experiments, which allowed us to measure their body motion and muscle activity during perturbed sitting. We used a nonlinear neuromechanical model of the seated human, along with a full-state feedback linearization approach and optimal control theory for identifying the neural control system and characterizing its task goals.Main results: We demonstrated that the neural feedback for trunk stability during seated posture uses angular position, velocity, acceleration, and jerk in a linearized space. The mean squared error between the predicted and measured motor commands was less than 0.6% among all trials and participants, with a median correlation coefficient (r) of more than 0.9. Our identified optimal neural control primarily used trunk angular acceleration and near-minimum muscle activation to achieve seated stability while keeping the deviations of the trunk angular position and acceleration sufficiently small. Significance: Our proposed approach to neural control system identification relied on a performance criterion (e.g., cost function) explaining what the functional goal is and subsequently, finds the control law that leads to the best performance. Therefore, instead of assuming what control schemes the neural control might utilize (e.g., proportional-integral-derivative control), optimal control allows the motor task and the neuromechanical model to dictate a control law that best describes the physiological process. This approach allows for a mechanistic understanding of the neuromuscular mechanisms involved in seated stability and for inferring the task goals used by the neural control system to achieve the targeted motor behaviour. Such neural control characterization can contribute to the development of objective balance evaluation tools and of bio-inspired assistive neuromodulation technologies.

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Section: Neural Control Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear neural control using feedback linearization explains task goals of central nervous system for trunk stability in sitting posture

Vette

Noamani

2023

J. Neural Eng.

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“…Most previous studies assumed time-invariance and linearity of the neuromechanical model for identifying the passive and active mechanisms of seated stability control [2,10,11,[15][16][17][18][19][20][21]. This assumption is valid only for small perturbation conditions around an upright posture.…”

Section: Importance Of Developing a Nonlinear Model Of Neuromuscular ...mentioning

confidence: 99%

“…using forgetting factors), which allows for tracking system variations due to external disturbances, muscle fatigue, and physiological uncertainties. Furthermore, the identification method used by previous studies [2,10,11,[15][16][17][18][19][20][21] does not address the adverse effect of time-varying process and measurement noise on the identification of the neuromechanical model. This adverse effect could impact the performance of assistive technologies in long-term operation, as characteristics of process and measurement noise change appreciably.…”

Section: Importance Of Developing a Nonlinear Model Of Neuromuscular ...mentioning

confidence: 99%

“…external forces, moving support surface, or moving visual surround) [15,17]. Previous work has identified the dynamics of standing stability [18,19], including passive as well as active control [2], sensory dynamics [16,20,21], muscular dynamics, and sensorimotor time-delay [2,15], and of seated stability both with [11] and without modeling the muscular dynamics [10,21]. Nevertheless, all previous work assumed time-invariant linear behavior for the neuromuscular control that maintains sitting or standing stability and neglected time-variant or nonlinear dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear response of human trunk musculature explains neuromuscular stabilization mechanisms in sitting posture

2022

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Objective: Determining the roles of underlying mechanisms involved in stabilizing the human trunk during sitting is a fundamental challenge in human motor control. However, distinguishing their roles requires understanding their complex interrelations and describing them with physiologically meaningful neuromechanical parameters. The literature has shown that such mechanistic understanding contributes to diagnosing and improving impaired balance as well as developing assistive technologies for restoring trunk stability. This study aimed to provide a comprehensive characterization of the underlying neuromuscular stabilization mechanisms involved in human sitting. Approach: This study characterized passive and active stabilization mechanisms involved in seated stability by identifying a nonlinear neuromechanical physiologically-meaningful model in ten able-bodied individuals during perturbed sitting via an adaptive unscented Kalman filter to account for the nonlinear time-varying process and measurement noises. Main results: We observed that the passive mechanism provided instant resistance against gravitational disturbances, whereas the active mechanism provided delayed complementary phasic response against external disturbances by activating appropriate trunk muscles while showing non-isometric behavior. The model predicted the trunk sway behavior during perturbed sitting with high accuracy and correlation (average: 0.0007 [rad2] and 86.77%). This allows a better mechanistic understanding of the roles of passive and active stabilization mechanisms involved in sitting. Significance: Our characterization approach accounts for the inherently nonlinear behavior of the neuromuscular mechanisms and physiological uncertainties, while allowing for real-time tracking and correction of parameters’ variations due to external disturbances and muscle fatigue. The outcome of our research, for the first time, (1) allows a better mechanistic understanding of the roles of passive and active stabilization mechanisms involved in sitting; (2) enables objective evaluation and targeted rehabilitative interventions for impaired balance; facilitate bio-inspired designs of assistive technologies, and (3) opens new horizons in mathematical identification of neuromechanical mechanisms employed in the stable control of human body postures and motions.

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Identification of Postural Controllers in Human Standing Balance

Wang

Bogert

2020

Journal of Biomechanical Engineering

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Standing balance is a simple motion task for healthy humans but the actions of the central nervous system (CNS) have not been described by generalized and sufficiently sophisticated control laws. While system identification approaches have been used to extracted models of the CNS, they either focus on short balance motions, leading to task-specific control laws, or assume that the standing balance system is linear. To obtain comprehensive control laws for human standing balance, complex balance motions, long duration tests, and nonlinear controller models are all needed. In this paper, we demonstrate that trajectory optimization with the direct collocation method can achieve these goals to identify complex CNS models for the human standing balance task. We first examined this identification method using synthetic motion data and showed that correct control parameters can be extracted. Then, six types of controllers, from simple linear to complex nonlinear, were identified from 100 seconds of motion data from randomly perturbed standing. Results showed that multiple time-delay paths and nonlinear properties are both needed in order to fully explain human feedback control of standing balance.

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Assessing Standing Balance using MIMO Closed Loop System Identification Techniques

Cited by 3 publications

References 17 publications

Nonlinear neural control using feedback linearization explains task goals of central nervous system for trunk stability in sitting posture

Nonlinear neural control using feedback linearization explains task goals of central nervous system for trunk stability in sitting posture

Nonlinear response of human trunk musculature explains neuromuscular stabilization mechanisms in sitting posture

Identification of Postural Controllers in Human Standing Balance

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