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| Preface | |
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| Introduction and Main Concepts | |
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| "Human Robotics" Approach to Model Human Motor Behavior | |
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| Outline: How Do We Learn to Control Motion? | |
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| Experimental Tools | |
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| Summary | |
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| Neural Control of Movement | |
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| Bioelectric Signal Transmission in the Nervous System | |
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| Information Processing in the Nervous System | |
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| Peripheral Sensory Receptors | |
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| Functional Control of Movement by the Central Nervous System | |
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| Summary | |
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| Muscle Mechanics and Control | |
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| The Molecular Basis of Force Generation in Muscle | |
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| The Molecular Basis of Viscoelasticity in Muscle | |
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| Control of Muscle Force | |
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| Muscle Bandwidth | |
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| Muscle Fiber Viscoelasticity | |
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| Muscle Geometry | |
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| Tendon Mechanics | |
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| Muscle-Tendon Unit | |
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| Summary | |
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| Single-Joint Neuromechanics | |
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| Joint Kinematics | |
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| Joint Mechanics | |
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| Joint Viscoelasticity and Mechanical Impedance | |
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| Sensory Feedback Control | |
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| Voluntary Movement | |
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| Summary | |
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| Multijoint Multimuscle Kinematics and Impedance | |
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| Kinematic Description | |
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| Planar Arm Motion | |
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| Direct and Inverse Kinematics | |
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| Differential Kinematics and Force Relationships | |
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| Mechanical Impedance | |
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| Kinematic Transformations | |
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| Impedance Geometry | |
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| Redundancy | |
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| Redundancy Resolution | |
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| Optimization with Additional Constraints | |
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| Posture Selection to Minimize Noise or Disturbance | |
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| Summary | |
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| Multijoint Dynamics and Motion Control | |
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| Human Movement Dynamics | |
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| Perturbation Dynamics during Movement | |
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| Linear and Nonlinear Robot Control | |
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| Feedforward Control Model | |
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| Impedance during Movement | |
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| Simulation of Reaching Movements in Novel Dynamics | |
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| Dynamic Redundancy | |
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| Nonlinear Adaptive Control of Robots | |
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| Radial-Basis Function (RBF) Neural Network Model | |
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| Summary | |
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| Motor Learning and Memory | |
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| Adaptation to Novel Dynamics | |
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| Sensory Signals Responsible for Motor Learning | |
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| Generalization in Motor Learning | |
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| Motor Memory | |
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| Modeling Learning of Stable Dynamics in Humans and Robots | |
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| Summary | |
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| Motor Learning under Unstable and Unpredictable Conditions | |
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| Motor Noise and Variability | |
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| Impedance Control for Unstable and Unpredictable Dynamics | |
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| Feedforward and Feedback Components of Impedance Control | |
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| Computational Algorithm for Motor Adaptation | |
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| Summary | |
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| Motion Planning and Online Control | |
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| Evidence of a Planning Stage | |
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| Coordinate Transformation | |
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| Optimal Movements | |
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| Task Error and Effort as a Natural Cost Function | |
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| Sensor-Based Motion Control | |
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| Linear Sensor Fusion | |
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| Stochastic Optimal Control Modeling of the Sensorimotor System | |
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| Reward-Based Optimal Control | |
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| Submotion Sensorimotor Primitives | |
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| Repetition versus Optimization in Tasks with Multiple Minima | |
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| Summary and Discussion on How to Learn Complex Behaviors | |
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| Integration and Control of Sensory Feedback | |
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| Bayesian Statistics | |
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| Forward Models | |
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| Purposeful Vision and Active Sensing | |
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| Adaptive Control of Feedback | |
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| Summary | |
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| Applications in Neurorehabilitation and Robotics | |
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| Neurorehabilitation | |
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| Motor Learning Principles in Rehabilitation | |
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| Robot-Assisted Rehabilitation of the Upper Extremities | |
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| Application of Neuroscience to Robot-Assisted Rehabilitation | |
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| Error Augmentation Strategies | |
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| Learning with Visual Substitution of Proprioceptive Error | |
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| Model of Motor Recovery after Stroke | |
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| Concurrent Force and Impedance Adaptation in Robots | |
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| Robotic Implementation | |
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| Humanlike Adaptation of Robotic Assistance for Active Learning | |
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| Summary and Conclusion | |
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| Appendix | |
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| References | |
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| Index | |