Human Motion Simulation

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