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Human Robotics: Neuromechanics and Motor Control [Mīkstie vāki]

(Imperial College London), (McGill University), (Technical University of Munich)
  • Formāts: Paperback / softback, 296 pages, height x width x depth: 229x178x13 mm, 104 b&w illus., 2 tables; 106 Illustrations
  • Sērija : The MIT Press
  • Izdošanas datums: 04-May-2018
  • Izdevniecība: MIT Press
  • ISBN-10: 0262536412
  • ISBN-13: 9780262536417
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 13,09 €
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  • Formāts: Paperback / softback, 296 pages, height x width x depth: 229x178x13 mm, 104 b&w illus., 2 tables; 106 Illustrations
  • Sērija : The MIT Press
  • Izdošanas datums: 04-May-2018
  • Izdevniecība: MIT Press
  • ISBN-10: 0262536412
  • ISBN-13: 9780262536417
Citas grāmatas par šo tēmu:
A synthesis of biomechanics and neural control that draws on recent advances in robotics to address control problems solved by the human sensorimotor system.


This book proposes a transdisciplinary approach to investigating human motor control that synthesizes musculoskeletal biomechanics and neural control. The authors argue that this integrated approach -- which uses the framework of robotics to understand sensorimotor control problems -- offers a more complete and accurate description than either a purely neural computational approach or a purely biomechanical one.

The authors offer an account of motor control in which explanatory models are based on experimental evidence using mathematical approaches reminiscent of physics. These computational models yield algorithms for motor control that may be used as tools to investigate or treat diseases of the sensorimotor system and to guide the development of algorithms and hardware that can be incorporated into products designed to assist with the tasks of daily living.

The authors focus on the insights their approach offers in understanding how movement of the arm is controlled and how the control adapts to changing environments. The book begins with muscle mechanics and control, progresses in a logical manner to planning and behavior, and describes applications in neurorehabilitation and robotics. The material is self-contained, and accessible to researchers and professionals in a range of fields, including psychology, kinesiology, neurology, computer science, and robotics.

Preface xi
1 Introduction and Main Concepts
1(14)
1.1 "Human Robotics" Approach to Model Human Motor Behavior
1(4)
1.2 Outline: How Do We Learn to Control Motion?
5(2)
1.3 Experimental Tools
7(6)
1.4 Summary
13(2)
2 Neural Control of Movement
15(20)
2.1 Bioelectric Signal Transmission in the Nervous System
15(4)
2.2 Information Processing in the Nervous System
19(2)
2.3 Peripheral Sensory Receptors
21(8)
2.4 Functional Control of Movement by the Central Nervous System
29(4)
2.5 Summary
33(2)
3 Muscle Mechanics and Control
35(22)
3.1 The Molecular Basis of Force Generation in Muscle
35(6)
3.2 The Molecular Basis of Viscoelasticity in Muscle
41(3)
3.3 Control of Muscle Force
44(4)
3.4 Muscle Bandwidth
48(1)
3.5 Muscle Fiber Viscoelasticity
49(2)
3.6 Muscle Geometry
51(2)
3.7 Tendon Mechanics
53(2)
3.8 Muscle-Tendon Unit
55(1)
3.9 Summary
56(1)
4 Single-Joint Neuromechanics
57(26)
4.1 Joint Kinematics
57(2)
4.2 Joint Mechanics
59(2)
4.3 Joint Viscoelasticity and Mechanical Impedance
61(1)
4.4 Sensory Feedback Control
62(11)
4.5 Voluntary Movement
73(5)
4.6 Summary
78(5)
5 Multijoint Multimuscle Kinematics and Impedance
83(28)
5.1 Kinematic Description
83(2)
5.2 Planar Arm Motion
85(1)
5.3 Direct and Inverse Kinematics
86(1)
5.4 Differential Kinematics and Force Relationships
87(3)
5.5 Mechanical Impedance
90(3)
5.6 Kinematic Transformations
93(2)
5.7 Impedance Geometry
95(4)
5.8 Redundancy
99(2)
5.9 Redundancy Resolution
101(1)
5.10 Optimization with Additional Constraints
102(3)
5.11 Posture Selection to Minimize Noise or Disturbance
105(2)
5.12 Summary
107(4)
6 Multijoint Dynamics and Motion Control
111(20)
6.1 Human Movement Dynamics
111(2)
6.2 Perturbation Dynamics during Movement
113(1)
6.3 Linear and Nonlinear Robot Control
113(2)
6.4 Feedforward Control Model
115(3)
6.5 Impedance during Movement
118(1)
6.6 Simulation of Reaching Movements in Novel Dynamics
118(2)
6.7 Dynamic Redundancy
120(4)
6.8 Nonlinear Adaptive Control of Robots
124(2)
6.9 Radial-Basis Function (RBF) Neural Network Model
126(3)
6.10 Summary
129(2)
7 Motor Learning and Memory
131(24)
7.1 Adaptation to Novel Dynamics
132(3)
7.2 Sensory Signals Responsible for Motor Learning
135(4)
7.3 Generalization in Motor Learning
139(6)
7.4 Motor Memory
145(6)
7.5 Modeling Learning of Stable Dynamics in Humans and Robots
151(2)
7.6 Summary
153(2)
8 Motor Learning under Unstable and Unpredictable Conditions
155(30)
8.1 Motor Noise and Variability
156(4)
8.2 Impedance Control for Unstable and Unpredictable Dynamics
160(10)
8.3 Feedforward and Feedback Components of Impedance Control
170(6)
8.4 Computational Algorithm for Motor Adaptation
176(6)
8.5 Summary
182(3)
9 Motion Planning and Online Control
185(26)
9.1 Evidence of a Planning Stage
185(3)
9.2 Coordinate Transformation
188(1)
9.3 Optimal Movements
189(2)
9.4 Task Error and Effort as a Natural Cost Function
191(2)
9.5 Sensor-Based Motion Control
193(3)
9.6 Linear Sensor Fusion
196(2)
9.7 Stochastic Optimal Control Modeling of the Sensorimotor System
198(4)
9.8 Reward-Based Optimal Control
202(2)
9.9 Submotion Sensorimotor Primitives
204(3)
9.10 Repetition versus Optimization in Tasks with Multiple Minima
207(2)
9.11 Summary and Discussion on How to Learn Complex Behaviors
209(2)
10 Integration and Control of Sensory Feedback
211(24)
10.1 Bayesian Statistics
212(8)
10.2 Forward Models
220(5)
10.3 Purposeful Vision and Active Sensing
225(2)
10.4 Adaptive Control of Feedback
227(6)
10.5 Summary
233(2)
11 Applications in Neurorehabilitation and Robotics
235(18)
11.1 Neurorehabilitation
235(1)
11.2 Motor Learning Principles in Rehabilitation
236(2)
11.3 Robot-Assisted Rehabilitation of the Upper Extremities
238(2)
11.4 Application of Neuroscience to Robot-Assisted Rehabilitation
240(1)
11.5 Error Augmentation Strategies
241(2)
11.6 Learning with Visual Substitution of Proprioceptive Error
243(2)
11.7 Model of Motor Recovery after Stroke
245(1)
11.8 Concurrent Force and Impedance Adaptation in Robots
246(1)
11.9 Robotic Implementation
247(2)
11.10 Humanlike Adaptation of Robotic Assistance for Active Learning
249(1)
11.11 Summary and Conclusion
250(3)
Appendix 253(4)
References 257(18)
Index 275