The effect of parameters of equilibrium-based 3-D biomechanical models on extracted muscle synergies during isometric lumbar exertion

Eskandari, Amir Hossein; Sedaghat-Nejad, Ehsan; Rashedi, Ehsan; Sedighi, Alireza; Arjmand, Navid; Parnianpour, Mohamad

doi:10.1016/j.jbiomech.2015.12.024

Cited by 9 publications

(7 citation statements)

References 27 publications

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“…In quasi-static conditions the emergent synergies responsible for a direction of external load will be linearly scaled. The invariance in set of activated muscles under varying magnitude of external load (Figure 6) is in line with the theories of using muscle synergy in multiple muscle systems across the cost functions (Moghadam et al, 2013;Eskandari et al, 2016). Future studies must test this in more physiological models with realistic posture/loading and non-linear properties.…”

Section: Interpretationssupporting

confidence: 70%

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Estimation of Trunk Muscle Forces Using a Bio-Inspired Control Strategy Implemented in a Neuro-Osteo-Ligamentous Finite Element Model of the Lumbar Spine

Sharifzadeh-Kermani

Arjmand

Vossoughi

et al. 2020

Front. Bioeng. Biotechnol.

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Section: Interpretationssupporting

confidence: 70%

“…The recruitment of trunk muscles has been shown to be strongly direction dependent (Nussbaum et al, 1995;Hadizadeh et al, 2014;Sedaghat-Nejad et al, 2015;Eskandari et al, 2016). In quasi-static conditions the emergent synergies responsible for a direction of external load will be linearly scaled.…”

Section: Interpretationsmentioning

confidence: 99%

Estimation of Trunk Muscle Forces Using a Bio-Inspired Control Strategy Implemented in a Neuro-Osteo-Ligamentous Finite Element Model of the Lumbar Spine

Sharifzadeh-Kermani

Arjmand

Vossoughi

et al. 2020

Front. Bioeng. Biotechnol.

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“…Different research groups have used different values and techniques to find the thresholds for VAF (Torres-Oviedo and Ting, 2010 ; Chvatal et al, 2011 ; Roh et al, 2013 ). In this study, four synergies considered to be enough to account for variability of the normal walking and slipping data (Figure 4 ) as they successfully reconstructed more than 75% of the original pooled data (VAF ≥ 0.75) and also addition of an extra synergy did not contribute in reconstruction of more than 5% of the original data (Figure 4 ; Chvatal and Ting, 2013 ; Eskandari et al, 2016 ). The local VAF curves (for each individual's data) also substantiated our choice of four muscle synergies, accounting for more than 95%, for both walking and slipping condition (Figure 5 ; Ting and Macpherson, 2005 ).…”

Section: Resultsmentioning

confidence: 89%

Shared and Task-Specific Muscle Synergies during Normal Walking and Slipping

Nazifi

Yoon

Beschorner

et al. 2017

Front. Hum. Neurosci.

102

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Falling accidents are costly due to their prevalence in the workplace. Slipping has been known to be the main cause of falling. Understanding the motor response used to regain balance after slipping is crucial to developing intervention strategies for effective recovery. Interestingly, studies on spinalized animals and studies on animals subjected to electrical microstimulation have provided major evidence that the Central Nervous System (CNS) uses motor primitives, such as muscle synergies, to control motor tasks. Muscle synergies are thought to be a critical mechanism used by the CNS to control complex motor tasks by reducing the dimensional complexity of the system. Even though synergies have demonstrated potential for indicating how the body responds to balance perturbations by accounting for majority of the data set's variability, this concept has not been applied to slipping. To address this gap, data from 11 healthy young adults were collected and analyzed during both unperturbed walking and slipping. Applying an iterative non-negative matrix decomposition technique, four muscle synergies and the corresponding time-series activation coefficients were extracted. The synergies and the activation coefficients were then compared between baseline walking and slipping to determine shared vs. task-specific synergies. Correlation analyses found that among four synergies, two synergies were shared between normal walking and slipping. However, the other two synergies were task-specific. Both limbs were contributing to each of the four synergies, suggesting substantial inter-limb coordination during gait and slip. These findings stay consistent with previous unilateral studies that reported similar synergies between unperturbed and perturbed walking. Activation coefficients corresponding to the two shared synergies were similar between normal walking and slipping for the first 200 ms after heel contact and differed later in stance, suggesting the activation of muscle synergies in response to a slip. A muscle synergy approach would reveal the used sub-tasks during slipping, facilitating identification of impaired sub-tasks, and potentially leading to a purposeful rehabilitation based on damaged sub-functions.

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“…Another important question in this field is the question of control variables or motor commands: what is the variable used by the command generation level and send to the execution level. In previous studies, motor commands of computational models were produced at different levels, they could be joint torques [12,19], muscle forces [26,27], muscle activities [28,29] or even recruitment coefficients (RCs) of the motor primitives [30,31]. Since changes in the joint torques stem actually from changes in muscle forces, so we believe that computational models in which the motor command is produced at the muscle space can represent performance of the CNS more clearly.…”

Section: Introductionmentioning

confidence: 99%

How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies

Dehghani

Bahrami

2020

PLoS ONE

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In this study, we introduce a hierarchical and modular computational model to explain how the CNS (Central Nervous System) controls arm reaching movement (ARM) in the frontal plane and under different conditions. The proposed hierarchical organization was established at three levels: 1) motor planning, 2) command production, and 3) motor execution. Since in this work we are not discussing motion learning, no learning procedure was considered in the model. Previous models mainly assume that the motor planning level produces the desired trajectories of the joints and feeds it to the next level to be tracked. In the proposed model, the motion control is described based on a regulatory control policy, that is, the output of the motor planning level is a step function defining the initial and final desired position of the hand. For the command production level, a nonlinear predictive model was developed to explain how the time-invariant muscle synergies (MSs) are recruited. We used the same computational model to explain the arm reaching motion for a combined ARM task. The combined ARM is defined as two successive ARM such that it starts from point A and reaches to point C via point B. To develop the model, kinematic and kinetic data from six subjects were recorded and analyzed during ARM task performance. The subjects used a robotic manipulator while moving their hand in the frontal plane. The EMG data of 15 muscles were also recorded. The MSs used in the model were extracted from the recorded EMG data. The proposed model explains two aspects of the motor control system by a novel computational approach: 1) the CNS reduces the dimension of the control space using the notion of MSs and thereby, avoids immense computational loads; 2) at the level of motor planning, the CNS generates the desired position of the hand at the starting, via and the final points, and this amounts to a regulatory and non-tracking structure.

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The effect of parameters of equilibrium-based 3-D biomechanical models on extracted muscle synergies during isometric lumbar exertion

Cited by 9 publications

References 27 publications

Estimation of Trunk Muscle Forces Using a Bio-Inspired Control Strategy Implemented in a Neuro-Osteo-Ligamentous Finite Element Model of the Lumbar Spine

Estimation of Trunk Muscle Forces Using a Bio-Inspired Control Strategy Implemented in a Neuro-Osteo-Ligamentous Finite Element Model of the Lumbar Spine

Shared and Task-Specific Muscle Synergies during Normal Walking and Slipping

How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies

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