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Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

10. Implications

verfasst von : Francisco J. Valero-Cuevas

Erschienen in: Fundamentals of Neuromechanics

Verlag: Springer London

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Abstract

This book is deliberately a short introduction to the mathematical and anatomical foundations of neuromechanics. My hope is that you will take these concepts and challenge, modify, extend, and leverage them to advance the science of neuromuscular control and its related areas, such as robotics, musculoskeletal modeling, computational neuroscience, rehabilitation, and evolutionary biology. Having established a common language, conceptual framework, and computational repertoire, I discuss several implications of this neuromechanical perspective. My intent is that my presentation of several issues, research directions, tenets, and debates, however brief, will inspire and encourage you in your research.

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Vorheriges Kapitel The Nature and Structure of Feasible Sets
Fußnoten
1
Very rarely do we find 2 muscles with identical lines of action and functional capabilities. I can think of the extensor indicis proprius and the slip of the extensor digitorum communis to the index finger as a potential example because they share the same tendon of insertion. But given the lack of strict independence of the muscle fibers of the latter muscle, perhaps the former muscle is needed to enforce functional independence of the index finger.
 
2
Model predictive control, or MPC, is a computationally intensive approach that solves multiple versions of the problem for a short time horizon into the future at each time step. It then uses cost and value functions to pick from among the multiple, most successful branches to assemble families of acceptable full trajectories to solve the problem.
 
3
In areas such as numerical analysis, optimization, sampling, combinatorics, machine learning, data mining, etc., as the dimensionality of the variables increases, the volume of the space increases so fast that available data become sparse. Thus the amount of data needed to obtain statistically sound and reliable results often grows exponentially with the dimensionality, rendering the problem impractical.
 
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Metadaten
Titel
Implications
verfasst von
Francisco J. Valero-Cuevas
Copyright-Jahr
2016
Verlag
Springer London
Buch
Fundamentals of Neuromechanics

Print ISBN: 978-1-4471-6746-4

Electronic ISBN: 978-1-4471-6747-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-1-4471-6747-1_10

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