Human Robotics Neuromechanics and Motor Control

ISBN-10: 0262019531

ISBN-13: 9780262019538

Edition: 2013

Authors: Etienne Burdet, David W. Franklin, Theodore E. Milner

List price: $50.00
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This book proposes a transdisciplinary approach to investigating human motor controlthat synthesizes musculoskeletal biomechanics and neural control. The authors argue that thisintegrated approach -- which uses the framework of robotics to understand sensorimotor controlproblems -- offers a more complete and accurate description than either a purely neuralcomputational approach or a purely biomechanical one.The authors offer an accountof motor control in which explanatory models are based on experimental evidence using mathematicalapproaches reminiscent of physics. These computational models yield algorithms for motor controlthat may be used as tools to investigate or treat diseases of the sensorimotor systems and to guidethe development of algorithms and hardware that can be incorporated into products designed to assistwith the tasks of daily living. The authors focus on the insights their approachoffers in understanding how movement of the arm is controlled and how the control adapts to changingenvironments. The book begins with muscle mechanics and control, progresses in a logical manner toplanning and behavior, and describes applications in neurorehabilitation and robotics. The materialis self-contained, and accessible to researchers and professionals in a range of fields, includingpsychology, kinesiology, neurology, computer science, and robotics.
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Book details

List price: $50.00
Copyright year: 2013
Publisher: MIT Press
Publication date: 9/13/2013
Binding: Hardcover
Pages: 296
Size: 7.00" wide x 9.00" long x 0.75" tall
Weight: 1.628
Language: English

Etienne Burdet is Reader in Human Robotics in the Department of Bioengineering at the ImperialCollege of Science, Technology, and Medicine, London.

David W. Franklin is Wellcome Trust Career Development Fellow in the Department of Engineeringat the University of Cambridge.

Theodore E. Milner is Professor in the Department of Kinesiology and Physical Education atMcGill University.

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