Human motion simulation : predictive dynamics

Title:

Personal Author:

Abdel-Malek, Karim

Publication Information:

Waltham, MA : Elsevier/Academic Press, c2013

Physical Description:

xix, 275 p. : ill. ; 25 cm.

ISBN:

9780124051904

Subject Term:

Human mechanics -- Simulation methods

Human body -- Simulation methods

Added Author:

Arora, Jasbir S.

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010321209	QP303 A23 2013	Open Access Book	Book	Searching... Unknown

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, such as speed and energy, which act as objective functions to be optimized. Constraints on joint torques and angles are imposed quite easily. Predicting motion in this way allows one to use avatars to study how and why humans move the way they do, given specific scenarios. It also enables avatars to react to infinitely many scenarios with substantial autonomy. With this approach it is possible to predict dynamic motion without having to integrate equations of motion -- rather than solving equations of motion, this approach solves for a continuous time-dependent curve characterizing joint variables (also called joint profiles) for every degree of freedom.

Author Notes

Karim Abdel-Malek is a professor in the Department of Biomedical Engineering and the Department of Mechanical and Industrial Engineering at the University of Iowa. He obtained his PhD in Mechanical Engineering from the University of Pennsylvania. Dr. Abdel-Malek is the Founder and Director of the Virtual Soldier Research (VSR) program; Director of the Center for Computer Aided Design; former Associate Editor of the International Journal of Robotics and Automation; former Editor-in-Chief of the International Journal of Human Factors Modeling Simulation; and a Fellow of the American Institute for Medical and Biological Engineering (AIMBE).
Jasbir Singh Arora is an F. Wendell Miller Professor of Engineering, a Professor of Civil and Environmental Engineering, and a Professor of Mechanical and Industrial Engineering at the University of Iowa. He obtained his PhD in Mechanics and Hydraulics from the University of Iowa. Dr. Arora is the Associate Director of the Center for Computer Aided Design. He is a Senior Advisor for the International Journal of Structural and Multidisciplinary Optimization and he is on the Editorial Board of the International Journal for Numerical Methods in Engineering. He is a Fellow of the American Society of Civil Engineers and the American Society of Mechanical Engineers, and a Senior Member of the American Institute of Aeronautics and Astronautics. Dr. Arora is an internationally recognized researcher in the field of optimization and his book Introduction to Optimum Design, 3rd Edition (Academic Press, 2012, 978-0-12-381375-6) is used worldwide.

Preface	p. xiii
Acknowledgments	p. xv
Chapter 1 Introduction	p. 1
1.1 What is predictive dynamics?	p. 1
1.2 How does predictive dynamics work?	p. 2
1.3 Why data-driven human motion prediction does not work	p. 3
1.4 Concluding remarks	p. 4
References	p. 5
Chapter 2 Human Modeling: Kinematics	p. 7
2.1 Introduction	p. 7
2.2 General rigid body displacement	p. 10
2.2.1 Example: rotation and translation	p. 11
2.3 Concept of extended vectors and homogeneous coordinates	p. 13
2.4 Basic transformations	p. 14
2.4.1 Example: knee rotation	p. 16
2.5 Composite transformations	p. 17
2.5.1 Example: composite transformations	p. 17
2.6 Directed transformation graphs	p. 19
2.6.1 Example: multiple transformations	p. 20
2.7 Determining the position of a multi-segmental link: forward kinematics	p. 24
2.8 The Denavit-Hartenberg representation	p. 25
2.9 The kinematic skeleton	p. 27
2.10 Establishing coordinate systems	p. 30
2.10.1 Example: a 9-DOF model of an upper limb	p. 31
2.10.2 Example: DH parameters of the lower limb	p. 32
2.11 The Santos® model	p. 36
2.12 Variations in anthropometry	p. 36
2.13 A 55-DOF whole body model	p. 37
2.14 Global DOFs and virtual joints	p. 39
2.15 Concluding remarks	p. 40
References	p. 40
Chapter 3 Posture Prediction and Optimization	p. 41
3.1 What is optimization?	p. 41
3.2 What is posture prediction?	p. 41
3.3 Inducing behavior	p. 43
3.4 Posture prediction versus inverse kinematics	p. 44
3.4.1 Analytical and geometric IK methods	p. 44
3.4.2 Empirically-based posture prediction	p. 44
3.5 Optimization-based posture prediction	p. 45
3.5.1 Design variables	p. 46
3.5.2 Constraints	p. 47
3.5.3 Cost function	p. 47
3.6 A 3-DOF arm example	p. 47
3.7 Development of human performance measures	p. 49
3.7.1 Joint displacement	p. 50
3.7.2 Effort	p. 50
3.7.3 Delta potential energy	p. 51
3.7.4 Discomfort	p. 53
3.7.5 Single-objective optimization	p. 55
3.7.6 Numerical solutions to optimization problems	p. 57
3.8 Motion between two points	p. 58
3.9 Joint profiles as B-spline curves	p. 58
3.10 Motion prediction formulation	p. 60
3.10.1 Design variables	p. 60
3.10.2 Constraints	p. 60
3.11 A 15-DOF motion prediction	p. 61
3.11.1 The 15-DOF Denavit-Hartenberg model	p. 61
3.12 Optimization algorithm	p. 62
3.13 Motion prediction of a 15-DOF model	p. 63
3.14 Multi-objective problem statement	p. 65
3.15 Design variables and constraints	p. 65
3.16 Concluding remarks	p. 65
References	p. 66
Chapter 4 Recursive Dynamics	p. 69
4.1 Introduction	p. 69
4.2 General static torque	p. 70
4.3 Dynamic equations of motion	p. 72
4.4 Formulation of regular Lagrangian equation	p. 74
4.4.1 Sensitivity analysis	p. 75
4.5 Recursive Lagrangian equations	p. 75
4.5.1 Forward recursive kinematics	p. 76
4.5.2 Backward recursive dynamics	p. 76
4.5.3 Sensitivity analysis	p. 77
4.5.4 Kinematics sensitivity analysis	p. 77
4.5.5 Dynamics sensitivity analysis	p. 78
4.5.6 Joint profile discretization	p. 80
4.6 Examples using a 2-DOF arm	p. 81
4.6.1 The DH parameters	p. 82
4.6.2 Forward recursive kinematics	p. 83
4.6.3 Backward recursive dynamics	p. 84
4.6.4 Gradients	p. 84
4.6.5 Closed-form equations of motion	p. 86
4.7 Trajectory planning example	p. 87
4.8 Arm lifting motion with load example	p. 88
4.9 Concluding remarks	p. 90
References	p. 92
Chapter 5 Predictive Dynamics	p. 95
5.1 Introduction	p. 95
5.2 Problem formulation	p. 95
5.3 Dynamic stability: zero-moment point	p. 99
5.4 Performance measures	p. 101
5.5 Inner optimization	p. 102
5.6 Constraints	p. 103
5.6.1 Feasible set	p. 104
5.6.2 Minimal set of constraints	p. 104
5.7 Types of constraints	p. 105
5.7.1 Time-dependent constraints	p. 105
5.7.2 Time-independent constraints	p. 107
5.8 Discretization and scaling	p. 108
5.9 Numerical example: single pendulum	p. 109
5.9.1 Description of the problem	p. 109
5.9.2 Simple swing motion with boundary conditions-PD solution	p. 111
5.9.3 Oscillating motion with boundary conditions-PD solution	p. 114
5.9.4 Oscillating motion with boundary conditions and one state-response constraint-PD solution	p. 116
5.9.5 Oscillating motion with boundary conditions and two state-response constraints	p. 118
5.10 Example formulations	p. 120
5.11 Concluding remarks	p. 120
References	p. 125
Chapter 6 Strength and Fatigue: Experiments and Modeling	p. 127
6.1 Joint space	p. 127
6.2 Strength influences	p. 128
6.3 Strength assessment	p. 132
6.4 Normative strength data	p. 134
6.5 Representing strength percentiles	p. 137
6.6 Mapping strength to digital humans: strength surfaces	p. 138
6.7 Fatigue	p. 140
6.8 Strength and fatigue interaction	p. 145
6.9 Concluding remarks	p. 145
References	p. 145
Chapter 7 Predicting the Biomechanics of Walking	p. 149
7.1 Introduction	p. 149
7.2 Joints as degrees of freedom (DOF)	p. 151
7.3 Muscle versus joint space	p. 151
7.4 Spatial kinematics model	p. 152
7.4.1 A kinematic 55-DOF human model	p. 152
7.4.2 Global DOFs and virtual joints	p. 154
7.4.3 Forward recursive kinematics	p. 155
7.5 Dynamics formulation	p. 156
7.5.1 Backward recursive dynamics	p. 156
7.5.2 Sensitivity analysis	p. 157
7.5.3 Mass and inertia property	p. 157
7.6 Gait model	p. 158
7.6.1 One-step gait model	p. 158
7.6.2 Ground reaction forces (GRF)	p. 159
7.7 Zero-Moment point (ZMP)	p. 161
7.7.1 Global forces at the pelvis	p. 162
7.7.2 Global forces at origin	p. 163
7.7.3 ZMP calculation	p. 163
7.8 Calculating ground reaction forces (GRF)	p. 164
7.9 Optimization formulation	p. 166
7.9.1 Design variables	p. 166
7.9.2 Objective function	p. 166
7.9.3 Constraints	p. 167
7.10 Numerical discretization	p. 171
7.11 Example: predicting the gait	p. 172
7.11.1 Normal walking	p. 172
7.12 Cause and effect	p. 176
7.13 Implementations of the predictive dynamics walking formulation	p. 183
7.13.1 Effect of constrained joints	p. 183
7.13.2 Sideways and backward walking	p. 183
7.13.3 Effect of changing anthropometry	p. 183
7.13.4 Effect of changing loads	p. 183
7.13.5 Walking on uneven terrains	p. 184
7.13.6 Asymmetric walking	p. 184
7.13.7 Walking on different terrain types	p. 184
7.14 Concluding remarks	p. 184
References	p. 185
Chapter 8 Predictive Dynamics: Lifting	p. 187
8.1 Human skeletal model	p. 187
8.2 Equations of motion and sensitivities	p. 187
8.2.1 Forward recursive kinematics	p. 187
8.2.2 Backward recursive dynamics	p. 189
8.2.3 Sensitivity analysis	p. 189
8.3 Dynamic stability and ground reaction forces (GRF)	p. 190
8.4 Formulation	p. 191
8.4.1 Lifting task	p. 191
8.5 Predictive dynamics optimization formulation	p. 192
8.5.1 Design variables and time discretization	p. 193
8.5.2 Objective functions	p. 194
8.5.3 Constraints	p. 194
8.6 Computational procedure for multi-objective optimization	p. 197
8.6.1 Lifting determinants and error quantification	p. 198
8.7 Predictive dynamics simulation	p. 199
8.8 Validation	p. 201
8.9 Concluding remarks	p. 204
References	p. 204
Chapter 9 Validation of Predictive Dynamics Tasks	p. 207
9.1 Introduction	p. 207
9.2 Motion determinants	p. 209
9.3 Motion capture systems	p. 209
9.3.1 Overview	p. 209
9.3.2 Optical motion capture systems	p. 210
9.3.3 Marker placement protocol	p. 211
9.3.4 Subject preparation and data collection	p. 212
9.4 Methods	p. 213
9.4.1 Normalizing the data	p. 213
9.4.2 Validation methodology	p. 214
9.5 Validation of predictive walking task	p. 216
9.5.1 Walking task description	p. 216
9.5.2 Walking determinants	p. 217
9.5.3 Participants	p. 217
9.5.4 Results	p. 217
9.6 Validation of box-lifting task	p. 224
9.6.1 Lifting task description	p. 224
9.6.2 Box-lifting determinants	p. 225
9.6.3 Participants	p. 225
9.6.4 Results	p. 225
9.7 Feedback to the simulation	p. 233
9.8 Concluding remarks	p. 233
References	p. 234
Chapter 10 Concluding Remarks	p. 237
10.1 Benefits of predictive dynamics	p. 237
10.1.1 Using the Denavit-Hartenberg (DH) method is effective in modeling human kinematics	p. 237
10.1.2 Predictive dynamics solves dynamics without integration	p. 238
10.1.3 Predictive dynamics renders natural motion	p. 238
10.1.4 Predictive dynamics induces natural behavior	p. 238
10.1.5 Predictive dynamics admits cause and effect	p. 238
10.1.6 Predictive dynamics uses joint space, not muscle space	p. 239
10.1.7 Predictive dynamics uses dynamic strength surfaces	p. 239
10.1.8 The PD validation process is effective	p. 240
10.2 Applications	p. 240
10.2.1 Ergonomics	p. 240
10.2.2 Simulating an injury or a disability	p. 240
10.2.3 Sports biomechanics and kinesiology	p. 241
10.2.4 Human performance	p. 241
10.2.5 Testing equipment, digital prototyping, human systems integration	p. 241
10.2.6 Egress/ingress	p. 242
10.2.7 Unsafe situations	p. 242
10.3 Future research	p. 243
10.3.1 Soft-tissue dynamics	p. 243
10.3.2 Intelligence	p. 243
10.3.3 Psychological and physiological factors	p. 243
10.3.4 Modeling with a high level of fidelity	p. 244
10.3.5 Real-time simulation	p. 244
Reference	p. 245
Bibliography	p. 247
Index	p. 269

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