LIMITED OFFER
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Simulate realistic human motion in a virtual world with an optimization-based approach to motion prediction. With this approach, motion is governed by human performance measures,… Read more
LIMITED OFFER
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Dedication
Preface
Acknowledgments
Current Faculty and Staff
Current Students
Past Students and Collaborators
Past Summer Interns
Visiting Faculty and Scientists
Chapter 1. Introduction
1.1 What is predictive dynamics?
1.2 How does predictive dynamics work?
1.3 Why data-driven human motion prediction does not work
1.4 Concluding remarks
References
Chapter 2. Human Modeling: Kinematics
2.1 Introduction
2.2 General rigid body displacement
2.3 Concept of extended vectors and homogeneous coordinates
2.4 Basic transformations
2.5 Composite transformations
2.6 Directed transformation graphs
2.7 Determining the position of a multi-segmental link: forward kinematics
2.8 The Denavit–Hartenberg representation
2.9 The kinematic skeleton
2.10 Establishing coordinate systems
2.11 The Santos® model
2.12 Variations in anthropometry
2.13 A 55-DOF whole body model
2.14 Global DOFs and virtual joints
2.15 Concluding remarks
References
Chapter 3. Posture Prediction and Optimization
3.1 What is optimization?
3.2 What is posture prediction?
3.3 Inducing behavior
3.4 Posture prediction versus inverse kinematics
3.5 Optimization-based posture prediction
3.6 A 3-DOF arm example
3.7 Development of human performance measures
3.8 Motion between two points
3.9 Joint profiles as B-spline curves
3.10 Motion prediction formulation
3.11 A 15-DOF motion prediction
3.12 Optimization algorithm
3.13 Motion prediction of a 15-DOF model
3.14 Multi-objective problem statement
3.15 Design variables and constraints
3.16 Concluding remarks
References
Chapter 4. Recursive Dynamics
4.1 Introduction
4.2 General static torque
4.3 Dynamic equations of motion
4.4 Formulation of regular Lagrangian equation
4.5 Recursive Lagrangian equations
4.6 Examples using a 2-DOF arm
4.7 Trajectory planning example
4.8 Arm lifting motion with load example
4.9 Concluding remarks
References
Chapter 5. Predictive Dynamics
5.1 Introduction
5.2 Problem formulation
5.3 Dynamic stability: zero-moment point
5.4 Performance measures
5.5 Inner optimization
5.6 Constraints
5.7 Types of constraints
5.8 Discretization and scaling
5.9 Numerical example: single pendulum
5.10 Example formulations
5.11 Concluding remarks
References
Chapter 6. Strength and Fatigue: Experiments and Modeling
6.1 Joint space
6.2 Strength influences
6.3 Strength assessment
6.4 Normative strength data
6.5 Representing strength percentiles
6.6 Mapping strength to digital humans: strength surfaces
6.7 Fatigue
6.8 Strength and fatigue interaction
6.9 Concluding remarks
References
Chapter 7. Predicting the Biomechanics of Walking
7.1 Introduction
7.2 Joints as degrees of freedom (DOF)
7.3 Muscle versus joint space
7.4 Spatial kinematics model
7.5 Dynamics formulation
7.6 Gait model
7.7 Zero-Moment point (ZMP)
7.8 Calculating ground reaction forces (GRF)
7.9 Optimization formulation
7.10 Numerical discretization
7.11 Example: predicting the gait
7.12 Cause and effect
7.13 Implementations of the predictive dynamics walking formulation
7.14 Concluding remarks
References
Chapter 8. Predictive Dynamics: Lifting
8.1 Human skeletal model
8.2 Equations of motion and sensitivities
8.3 Dynamic stability and ground reaction forces (GRF)
8.4 Formulation
8.5 Predictive dynamics optimization formulation
8.6 Computational procedure for multi-objective optimization
8.7 Predictive dynamics simulation
8.8 Validation
8.9 Concluding remarks
References
Chapter 9. Validation of Predictive Dynamics Tasks
9.1 Introduction
9.2 Motion determinants
9.3 Motion capture systems
9.4 Methods
9.5 Validation of predictive walking task
9.6 Validation of box-lifting task
9.7 Feedback to the simulation
9.8 Concluding remarks
References
Chapter 10. Concluding Remarks
10.1 Benefits of predictive dynamics
10.2 Applications
10.3 Future research
Reference
Bibliography
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Index
KA
JA