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Fundamentals of Neuromechanics 1st ed. 2015 [Kõva köide]

  • Formaat: Hardback, 194 pages, kõrgus x laius: 235x155 mm, kaal: 4557 g, 65 Illustrations, black and white; XXIV, 194 p. 65 illus., 1 Hardback
  • Sari: Biosystems & Biorobotics 8
  • Ilmumisaeg: 23-Sep-2015
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1447167465
  • ISBN-13: 9781447167464
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  • Formaat: Hardback, 194 pages, kõrgus x laius: 235x155 mm, kaal: 4557 g, 65 Illustrations, black and white; XXIV, 194 p. 65 illus., 1 Hardback
  • Sari: Biosystems & Biorobotics 8
  • Ilmumisaeg: 23-Sep-2015
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1447167465
  • ISBN-13: 9781447167464
This book provides a conceptual and computational framework to study how the nervous system exploits the anatomical properties of limbs to produce mechanical function. The study of the neural control of limbs has historically emphasized the use of optimization to find solutions to the muscle redundancy problem. That is, how does the nervous system select a specific muscle coordination pattern when the many muscles of a limb allow for multiple solutions? I revisit this problem from the emerging perspective of neuromechanics that emphasizes finding and implementing families of feasible solutions, instead of a single and unique optimal solution. Those families of feasible solutions emerge naturally from the interactions among the feasible neural commands, anatomy of the limb, and constraints of the task. Such alternative perspective to the neural control of limb function is not only biologically plausible, but sheds light on the most central tenets and debates in the fields of neural control, robotics, rehabilitation, and brain-body co-evolutionary adaptations. This perspective developed from courses I taught to engineers and life scientists at Cornell University and the University of Southern California, and is made possible by combining fundamental concepts from mechanics, anatomy, mathematics, robotics and neuroscience with advances in the field of computational geometry. Fundamentals of Neuromechanics is intended for neuroscientists, roboticists, engineers, physicians, evolutionary biologists, athletes, and physical and occupational therapists seeking to advance their understanding of neuromechanics. Therefore, the tone is decidedly pedagogical, engaging, integrative, and practical to make it accessible to people coming from a broad spectrum of disciplines. I attempt to tread the line between making the mathematical exposition accessible to life scientists, and convey the wonder and complexity of neuroscience to engineers and computational scientists. While no one approach can hope to definitively resolve the important questions in these related fields, I hope to provide you with the fundamental background and tools to allow you to contribute to the emerging field of neuromechanics.
1 Introduction
1(8)
Part I Fundamentals
2 Limb Kinematics
9(16)
2.1 What Is a Limb?
9(3)
2.2 Forward Kinematic Analysis of Limbs
12(1)
2.3 The Forward Kinematic Model
13(2)
2.4 Application of the Forward Kinematic Model to a Simple Planar Limb
15(4)
2.5 Using the Forward Kinematic Model to Obtain Endpoint Velocities
19(1)
2.6 General Case of the Jacobian in the Context of Screws, Twists, and Wrenches
20(2)
2.7 Using the Jacobian of a Planar System to Find Endpoint Velocities
22(2)
2.8 Exercises and Computer Code
24(1)
References
24(1)
3 Limb Mechanics
25(12)
3.1 Derivation of the Relationship Between Static Endpoint Forces and Joint Torques
25(2)
3.2 Symbolic Example Finding All Permutations of J for a Planar 2 DOF Limb
27(1)
3.3 Numerical Example Finding All Permutations of J for a Planar 2 DOF Limb
28(2)
3.4 Relationship Between JT and the Equations of Static Equilibrium
30(1)
3.5 Importance of Understanding the Kinematic Degrees of Freedom of a Limb
31(1)
3.6 Analysis of a Planar 3 DOF Limb
32(2)
3.7 Additional Comments on the Jacobian and Its Properties
34(1)
3.8 Exercises and Computer Code
35(2)
References
36(1)
4 Tendon-Driven Limbs
37(18)
4.1 Tendon Actuation
38(1)
4.2 Tendon Routing, Skeletal Geometry, and Moment Arms
39(2)
4.3 Tendon Excursion
41(2)
4.4 Two or More Tendons Acting on a Joint: Under- and Overdetermined Systems
43(2)
4.5 The Moment Arm Matrix for Torque Production
45(3)
4.6 The Moment Arm Matrix for Tendon Excursions
48(2)
4.7 Implications to the Neural Control of Tendon-Driven Limbs
50(1)
4.8 Exercises and Computer Code
51(4)
References
51(4)
Part II Introduction to the Neural Control of Tendon-Driven Limbs
5 The Neural Control of Joint Torques in Tendon-Driven Limbs Is Underdetermined
55(16)
5.1 Muscle Activation and Redundancy of Neural Control
55(3)
5.2 Linear Programming Applied to Tendon-Driven Limbs
58(5)
5.2.1 Canonical Formulation of the Linear Programming Problem
59(1)
5.2.2 A Classical Example of Linear Programming: The Diet Problem
59(4)
5.3 Linear Programming Applied to Neuromuscular Problems
63(2)
5.4 Geometric Interpretation of Linear Programming
65(3)
5.5 Exercises and Computer Code
68(3)
References
69(2)
6 The Neural Control of Musculotendon Lengths and Excursions Is Overdetermined
71(20)
6.1 Forward and Inverse Kinematics of a Limb
71(1)
6.2 Forward Kinematics of a 5 DOF Arm
72(3)
6.3 Inverse Kinematics of a Limb
75(2)
6.3.1 Closed Form Analytical Approach
75(1)
6.3.2 Numerical Approach
76(1)
6.3.3 Experimental Approach
76(1)
6.4 The Overdetermined Problem of Tendon Excursions
77(4)
6.4.1 Tendon Excursion Versus Musculotendon Excursion
77(1)
6.4.2 Muscle Mechanics
78(3)
6.4.3 Reflex Mechanisms Interact with Limb Kinematics, Mechanics, and Muscle Properties
81(1)
6.5 Example of a Disc Throw Motion with a 17-Muscle, 5 DOF Arm
81(2)
6.6 Implications to Neural Control and Muscle Redundancy
83(2)
6.7 Exercises and Computer Code
85(6)
References
85(6)
Part III Feasible Actions of Tendon-Driven Limbs
7 Feasible Neural Commands and Feasible Mechanical Outputs
91(22)
7.1 Mapping from Neural Commands to Mechanical Outputs
91(3)
7.2 Geometric Interpretation of Feasibility
94(3)
7.3 Introduction to Feasible Sets
97(4)
7.4 Calculating Feasible Sets for Tasks with No Functional Constraints
101(4)
7.5 Size and Shape of Feasible Sets
105(2)
7.6 Anatomy of a Convex Polygon, Polyhedron, and Polytope
107(3)
7.7 Exercises and Computer Code
110(3)
References
110(3)
8 Feasible Neural Commands with Mechanical Constraints
113(22)
8.1 Finding Unique Optimal Solutions Versus Finding Families of Valid Solutions
113(3)
8.2 Calculating Feasible Sets for Tasks with Functional Constraints
116(3)
8.3 Vertex Enumeration in Practice
119(5)
8.4 A Definition of Versatility
124(1)
8.5 How Many Muscles Should Limbs Have to be Versatile?
125(2)
8.6 Limb Versatility Versus Muscle Redundancy
127(2)
8.7 Exercises and Computer Code
129(6)
References
129(6)
Part IV Neuromechanics as a Scientific Tool
9 The Nature and Structure of Feasible Sets
135(24)
9.1 Bounding Box Description of Feasible Sets
135(2)
9.2 Principal Components Analysis Description of Feasible Sets
137(3)
9.3 Synergy-Based Description of Feasible Sets
140(3)
9.4 Vectormap Description of Feasible Sets
143(6)
9.5 Probabilistic Neural Control
149(5)
9.6 Exercises and Computer Code
154(5)
References
154(5)
10 Implications
159(16)
10.1 Muscle Redundancy
159(3)
10.2 What This Book Did Not Cover
162(1)
10.3 What's in a Name?
163(3)
10.4 Motion, Force, and Impedance
166(2)
10.5 Agonist Versus Antagonist
168(1)
10.6 Co-contraction
169(3)
10.7 Exercises and Computer Code
172(3)
References
172(3)
Appendix A Primer on Linear Algebra and the Kinematics of Rigid Bodies 175(16)
Index 191
Francisco Valero-Cuevas is a Full Professor in the Department of Biomedical Engineering, and the Division of Biokinesiology & Physical Therapy at the University of Southern California. He also holds appointments in the departments of Aerospace & Mechanical Engineering and Computer Science. Prior to this he was Assistant and Associate Professor at Cornell University.

He holds a Bachelors degree in Engineering Science from Swarthmore College, a Masters degree from Queens University, and a Doctoral degree in Mechanical Engineering focused on neuroscience from Stanford University.

He has been visiting professor at the Max Planck Institute in Munich, Germany, ETH-Zurich, Switzerland, and the Institute of Sports Sciences in Innsbruck, Austria. He has served as Associate Editor of the IEEE Transactions on Biomedical Engineering and Guest Editor of PLoS Computational Biology. In 2013 he was elected Senior Member of the IEEE, and in 2014 to the College of Fellows of the American Institute for Medical and Biological Engineers.

His research focuses on an integrative approach to brain-body interactions for versatile function in machines and organisms.