2015
DOI: 10.1088/1748-3190/10/5/056016
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Abstract: In the field of human motor control, the motor synergy hypothesis explains how humans simplify body control dimensionality by coordinating groups of muscles, called motor synergies, instead of controlling muscles independently. In most applications of motor synergies to low-dimensional control in robotics, motor synergies are extracted from given optimal control signals. In this paper, we address the problems of how to extract motor synergies without optimal data given, and how to apply motor synergies to achi… Show more

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Cited by 4 publications
(3 citation statements)
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“…Although this framework was successful in real-time control of the motion in the task space, the redundant degrees of freedom were essentially left uncontrolled. Similar studies that considered task/synergy relationships (e.g., D'Avella et al, 2008; Berger and D'Avella, 2014; Fu et al, 2015) also lack discussion of the redundant DoFs. The present paper introduces an extension to the motor control framework of Sharif Razavian et al (2019) by proposing how the same framework can be used to control the redundant degrees of freedom alongside the task-related ones.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Although this framework was successful in real-time control of the motion in the task space, the redundant degrees of freedom were essentially left uncontrolled. Similar studies that considered task/synergy relationships (e.g., D'Avella et al, 2008; Berger and D'Avella, 2014; Fu et al, 2015) also lack discussion of the redundant DoFs. The present paper introduces an extension to the motor control framework of Sharif Razavian et al (2019) by proposing how the same framework can be used to control the redundant degrees of freedom alongside the task-related ones.…”
Section: Discussionmentioning
confidence: 99%
“…Stanev and Moustakas (2017) have employed a task-space formulation and an optimization routine to solve for the muscle activations. Fu et al (2015) have developed a controller for a kinematically redundant system; however, only the control of the task-variables are reported. The only available computational framework that formulates the kinematic and dynamic redundancies in musculoskeletal systems is developed by Stanev and Moustakas (2019); however, no direct relationship between the muscle redundancy and kinematic redundancy is discussed.…”
Section: Introductionmentioning
confidence: 99%
“…A few control models are presented that have the potential for feedback motion control of complex biomechanical systems. Among the published approaches are the controllers based on artificial neural networks [15], [16], advanced optimal controllers [17]- [19], and controllers based on muscle synergies and task-space [20], [21]. Most of these controllers require detailed knowledge about the dynamics of the system, or rely on extensive training data.…”
Section: Introductionmentioning
confidence: 99%