Cover image for Human robotics : neuromechanics and motor control
Title:
Human robotics : neuromechanics and motor control
Publication Information:
Cambridge, Massachusetts : The MIT Press, 2013
Physical Description:
xii, 277 pages : illustrations ; 24 cm.
ISBN:
9780262019538

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PSZ JB 840926-2001 XX(840926.1) Book
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PSZ JB 30000010345144 QP357.5 B87 2013 Open Access Book Book
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PSZ KL 33000000016472 QP357.5 B87 2013 Open Access Book
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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

Prefacep. xi
1 Introduction and Main Conceptsp. 1
1.1 "Human Robotics" Approach to Model Human Motor Behaviorp. 1
1.2 Outline: How Do We Learn to Control Motion?p. 5
1.3 Experimental Toolsp. 7
1.4 Summaryp. 13
2 Neural Control of Movementp. 15
2.1 Bioelectric Signal Transmission in the Nervous Systemp. 15
2.2 Information Processing in the Nervous Systemp. 19
2.3 Peripheral Sensory Receptorsp. 21
2.4 Functional Control of Movement by the Central Nervous Systemp. 29
2.5 Summaryp. 33
3 Muscle Mechanics and Controlp. 35
3.1 The Molecular Basis of Force Generation in Musclep. 35
3.2 The Molecular Basis of Viscoelasticity in Musclep. 41
3.3 Control of Muscle Forcep. 44
3.4 Muscle Bandwidthp. 48
3.5 Muscle Fiber Viscoelasticityp. 49
3.6 Muscle Geometryp. 51
3.7 Tendon Mechanicsp. 53
3.8 Muscle-Tendon Unitp. 55
3.9 Summaryp. 56
4 Single-Joint Neuromechanicsp. 57
4.1 Joint Kinematicsp. 57
4.2 Joint Mechanicsp. 59
4.3 Joint Viscoelasticity and Mechanical Impedancep. 61
4.4 Sensory Feedback Controlp. 62
4.5 Voluntary Movementp. 73
4.6 Summaryp. 78
5 Multijoint Multimuscle Kinematics and Impedancep. 83
5.1 Kinematic Descriptionp. 83
5.2 Planar Arm Motionp. 85
5.3 Direct and Inverse Kinematicsp. 86
5.4 Differential Kinematics and Force Relationshipsp. 87
5.5 Mechanical Impedancep. 90
5.6 Kinematic Transformationsp. 93
5.7 Impedance Geometryp. 95
5.8 Redundancyp. 99
5.9 Redundancy Resolutionp. 101
5.10 Optimization with Additional Constraintsp. 102
5.11 Posture Selection to Minimize Noise or Disturbancep. 105
5.12 Summaryp. 107
6 Multijoint Dynamics and Motion Controlp. 111
6.1 Human Movement Dynamicsp. 111
6.2 Perturbation Dynamics during Movementp. 113
6.3 Linear and Nonlinear Robot Controlp. 113
6.4 Feedforward Control Modelp. 115
6.5 Impedance during Movementp. 118
6.6 Simulation of Reaching Movements in Novel Dynamicsp. 118
6.7 Dynamic Redundancyp. 120
6.8 Nonlinear Adaptive Control of Robotsp. 124
6.9 Radial-Basis Function (RBF) Neural Network Modelp. 126
6.10 Summaryp. 129
7 Motor Learning and Memoryp. 131
7.1 Adaptation to Novel Dynamicsp. 132
7.2 Sensory Signals Responsible for Motor Learningp. 135
7.3 Generalization in Motor Learningp. 139
7.4 Motor Memoryp. 145
7.5 Modeling Learning of Stable Dynamics in Humans and Robotsp. 151
7.6 Summaryp. 153
8 Motor Learning under Unstable and Unpredictable Conditionsp. 155
8.1 Motor Noise and Variabilityp. 156
8.2 Impedance Control for Unstable and Unpredictable Dynamicsp. 160
8.3 Feedforward and Feedback Components of Impedance Controlp. 170
8.4 Computational Algorithm for Motor Adaptationp. 176
8.5 Summaryp. 182
9 Motion Planning and Online Controlp. 185
9.1 Evidence of a Planning Stagep. 185
9.2 Coordinate Transformationp. 188
9.3 Optimal Movementsp. 189
9.4 Task Error and Effort as a Natural Cost Functionp. 191
9.5 Sensor-Based Motion Controlp. 193
9.6 Linear Sensor Fusionp. 196
9.7 Stochastic Optimal Control Modeling of the Sensorimotor Systemp. 198
9.8 Reward-Based Optimal Controlp. 202
9.9 Submotion Sensorimotor Primitivesp. 204
9.10 Repetition versus Optimization in Tasks with Multiple Minimap. 207
9.11 Summary and Discussion on How to Learn Complex Behaviorsp. 209
10 Integration and Control of Sensory Feedbackp. 211
10.1 Bayesian Statisticsp. 212
10.2 Forward Modelsp. 220
10.3 Purposeful Vision and Active Sensingp. 225
10.4 Adaptive Control of Feedbackp. 227
10.5 Summaryp. 233
11 Applications in Neurorehabilitation and Roboticsp. 235
11.1 Neurorehabilitationp. 235
11.2 Motor Learning Principles in Rehabilitationp. 236
11.3 Robot-Assisted Rehabilitation of the Upper Extremitiesp. 238
11.4 Application of Neuroscience to Robot-Assisted Rehabilitationp. 240
11.5 Error Augmentation Strategiesp. 241
11.6 Learning with Visual Substitution of Proprioceptive Errorp. 243
11.7 Model of Motor Recovery after Strokep. 245
11.8 Concurrent Force and Impedance Adaptation in Robotsp. 246
11.9 Robotic Implementationp. 247
11.10 Humanlike Adaptation of Robotic Assistance for Active Learningp. 249
11.11 Summary and Conclusionp. 250
Appendixp. 253
Referencesp. 257
Indexp. 275