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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 840926-2001 | XX(840926.1) | Book | Book | Searching... |
Searching... | 30000010345144 | QP357.5 B87 2013 | Open Access Book | Book | Searching... |
Searching... | 33000000016472 | QP357.5 B87 2013 | Open Access Book | Book | Searching... |
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Summary
Summary
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.
Author Notes
Etienne Burdet is Professor of Human Robotics in the Department of Bioengineering at the Imperial College of Science, Technology, and Medicine, London. David W. Franklin is Wellcome Trust Career Development Fellow in the Department of Engineering at the University of Cambridge. Theodore E. Milner is Professor in the Department of Kinesiology and Physical Education at McGill University.
Table of Contents
Preface | p. xi |
1 Introduction and Main Concepts | p. 1 |
1.1 "Human Robotics" Approach to Model Human Motor Behavior | p. 1 |
1.2 Outline: How Do We Learn to Control Motion? | p. 5 |
1.3 Experimental Tools | p. 7 |
1.4 Summary | p. 13 |
2 Neural Control of Movement | p. 15 |
2.1 Bioelectric Signal Transmission in the Nervous System | p. 15 |
2.2 Information Processing in the Nervous System | p. 19 |
2.3 Peripheral Sensory Receptors | p. 21 |
2.4 Functional Control of Movement by the Central Nervous System | p. 29 |
2.5 Summary | p. 33 |
3 Muscle Mechanics and Control | p. 35 |
3.1 The Molecular Basis of Force Generation in Muscle | p. 35 |
3.2 The Molecular Basis of Viscoelasticity in Muscle | p. 41 |
3.3 Control of Muscle Force | p. 44 |
3.4 Muscle Bandwidth | p. 48 |
3.5 Muscle Fiber Viscoelasticity | p. 49 |
3.6 Muscle Geometry | p. 51 |
3.7 Tendon Mechanics | p. 53 |
3.8 Muscle-Tendon Unit | p. 55 |
3.9 Summary | p. 56 |
4 Single-Joint Neuromechanics | p. 57 |
4.1 Joint Kinematics | p. 57 |
4.2 Joint Mechanics | p. 59 |
4.3 Joint Viscoelasticity and Mechanical Impedance | p. 61 |
4.4 Sensory Feedback Control | p. 62 |
4.5 Voluntary Movement | p. 73 |
4.6 Summary | p. 78 |
5 Multijoint Multimuscle Kinematics and Impedance | p. 83 |
5.1 Kinematic Description | p. 83 |
5.2 Planar Arm Motion | p. 85 |
5.3 Direct and Inverse Kinematics | p. 86 |
5.4 Differential Kinematics and Force Relationships | p. 87 |
5.5 Mechanical Impedance | p. 90 |
5.6 Kinematic Transformations | p. 93 |
5.7 Impedance Geometry | p. 95 |
5.8 Redundancy | p. 99 |
5.9 Redundancy Resolution | p. 101 |
5.10 Optimization with Additional Constraints | p. 102 |
5.11 Posture Selection to Minimize Noise or Disturbance | p. 105 |
5.12 Summary | p. 107 |
6 Multijoint Dynamics and Motion Control | p. 111 |
6.1 Human Movement Dynamics | p. 111 |
6.2 Perturbation Dynamics during Movement | p. 113 |
6.3 Linear and Nonlinear Robot Control | p. 113 |
6.4 Feedforward Control Model | p. 115 |
6.5 Impedance during Movement | p. 118 |
6.6 Simulation of Reaching Movements in Novel Dynamics | p. 118 |
6.7 Dynamic Redundancy | p. 120 |
6.8 Nonlinear Adaptive Control of Robots | p. 124 |
6.9 Radial-Basis Function (RBF) Neural Network Model | p. 126 |
6.10 Summary | p. 129 |
7 Motor Learning and Memory | p. 131 |
7.1 Adaptation to Novel Dynamics | p. 132 |
7.2 Sensory Signals Responsible for Motor Learning | p. 135 |
7.3 Generalization in Motor Learning | p. 139 |
7.4 Motor Memory | p. 145 |
7.5 Modeling Learning of Stable Dynamics in Humans and Robots | p. 151 |
7.6 Summary | p. 153 |
8 Motor Learning under Unstable and Unpredictable Conditions | p. 155 |
8.1 Motor Noise and Variability | p. 156 |
8.2 Impedance Control for Unstable and Unpredictable Dynamics | p. 160 |
8.3 Feedforward and Feedback Components of Impedance Control | p. 170 |
8.4 Computational Algorithm for Motor Adaptation | p. 176 |
8.5 Summary | p. 182 |
9 Motion Planning and Online Control | p. 185 |
9.1 Evidence of a Planning Stage | p. 185 |
9.2 Coordinate Transformation | p. 188 |
9.3 Optimal Movements | p. 189 |
9.4 Task Error and Effort as a Natural Cost Function | p. 191 |
9.5 Sensor-Based Motion Control | p. 193 |
9.6 Linear Sensor Fusion | p. 196 |
9.7 Stochastic Optimal Control Modeling of the Sensorimotor System | p. 198 |
9.8 Reward-Based Optimal Control | p. 202 |
9.9 Submotion Sensorimotor Primitives | p. 204 |
9.10 Repetition versus Optimization in Tasks with Multiple Minima | p. 207 |
9.11 Summary and Discussion on How to Learn Complex Behaviors | p. 209 |
10 Integration and Control of Sensory Feedback | p. 211 |
10.1 Bayesian Statistics | p. 212 |
10.2 Forward Models | p. 220 |
10.3 Purposeful Vision and Active Sensing | p. 225 |
10.4 Adaptive Control of Feedback | p. 227 |
10.5 Summary | p. 233 |
11 Applications in Neurorehabilitation and Robotics | p. 235 |
11.1 Neurorehabilitation | p. 235 |
11.2 Motor Learning Principles in Rehabilitation | p. 236 |
11.3 Robot-Assisted Rehabilitation of the Upper Extremities | p. 238 |
11.4 Application of Neuroscience to Robot-Assisted Rehabilitation | p. 240 |
11.5 Error Augmentation Strategies | p. 241 |
11.6 Learning with Visual Substitution of Proprioceptive Error | p. 243 |
11.7 Model of Motor Recovery after Stroke | p. 245 |
11.8 Concurrent Force and Impedance Adaptation in Robots | p. 246 |
11.9 Robotic Implementation | p. 247 |
11.10 Humanlike Adaptation of Robotic Assistance for Active Learning | p. 249 |
11.11 Summary and Conclusion | p. 250 |
Appendix | p. 253 |
References | p. 257 |
Index | p. 275 |