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E-grāmata: Human Motion Simulation: Predictive Dynamics

(Professor of Biomedical Engineering and Mechanical & Industrial Engineering, University of Iowa), (Department of Civil and Environmental Engineering & Department of Mechanical Engineering, University of Iowa)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 30-May-2013
  • Izdevniecība: Academic Press Inc
  • Valoda: eng
  • ISBN-13: 9780124046016
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Human Motion Simulation: Predictive DynamicsPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 30-May-2013
  • Izdevniecība: Academic Press Inc
  • Valoda: eng
  • ISBN-13: 9780124046016
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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.

  • Introduces rigorous mathematical methods for digital human modelling and simulation
  • Focuses on understanding and representing spatial relationships (3D) of biomechanics
  • Develops an innovative optimization-based approach to predicting human movement
  • Extensively illustrated with 3D images of simulated human motion (full color in the ebook version)

Papildus informācija

Predict realistic human motion without the need for pre-recorded data or animations with this optimization-based approach to human simulation.
Preface xiii
Acknowledgments xv
Chapter 1 Introduction
1(6)
1.1 What is predictive dynamics?
1(1)
1.2 How does predictive dynamics work?
2(1)
1.3 Why data-driven human motion prediction does not work
3(1)
1.4 Concluding remarks
4(3)
References
5(2)
Chapter 2 Human Modeling: Kinematics
7(34)
2.1 Introduction
7(3)
2.2 General rigid body displacement
10(3)
2.2.1 Example: rotation and translation
11(2)
2.3 Concept of extended vectors and homogeneous coordinates
13(1)
2.4 Basic transformations
14(3)
2.4.1 Example: knee rotation
16(1)
2.5 Composite transformations
17(2)
2.5.1 Example: composite transformations
17(2)
2.6 Directed transformation graphs
19(5)
2.6.1 Example: multiple transformations
20(4)
2.7 Determining the position of a multi-segmental link: forward kinematics
24(1)
2.8 The Denavit---Hartenberg representation
25(2)
2.9 The kinematic skeleton
27(3)
2.10 Establishing coordinate systems
30(6)
2.10.1 Example: a 9-DOF model of an upper limb
31(1)
2.10.2 Example: DH parameters of the lower limb
32(4)
2.11 The Santos® model
36(1)
2.12 Variations in anthropometry
36(1)
2.13 A 55-DOF whole body model
37(2)
2.14 Global DOFs and virtual joints
39(1)
2.15 Concluding remarks
40(1)
References
40(1)
Chapter 3 Posture Prediction and Optimization
41(28)
3.1 What is optimization?
41(1)
3.2 What is posture prediction?
41(2)
3.3 Inducing behavior
43(1)
3.4 Posture prediction versus inverse kinematics
44(1)
3.4.1 Analytical and geometric IK methods
44(1)
3.4.2 Empirically-based posture prediction
44(1)
3.5 Optimization-based posture prediction
45(2)
3.5.1 Design variables
46(1)
3.5.2 Constraints
47(1)
3.5.3 Cost function
47(1)
3.6 A 3-DOF arm example
47(2)
3.7 Development of human performance measures
49(9)
3.7.1 Joint displacement
50(1)
3.7.2 Effort
50(1)
3.7.3 Delta potential energy
51(2)
3.7.4 Discomfort
53(2)
3.7.5 Single-objective optimization
55(2)
3.7.6 Numerical solutions to optimization problems
57(1)
3.8 Motion between two points
58(1)
3.9 Joint profiles as B-spline curves
58(2)
3.10 Motion prediction formulation
60(1)
3.10.1 Design variables
60(1)
3.10.2 Constraints
60(1)
3.11 A 15-DOF motion prediction
61(1)
3.11.1 The 15-DOF Denavit-Hartenberg model
61(1)
3.12 Optimization algorithm
62(1)
3.13 Motion prediction of a 15-DOF model
63(2)
3.14 Multi-objective problem statement
65(1)
3.15 Design variables and constraints
65(1)
3.16 Concluding remarks
65(4)
References
66(3)
Chapter 4 Recursive Dynamics
69(26)
4.1 Introduction
69(1)
4.2 General static torque
70(2)
4.3 Dynamic equations of motion
72(2)
4.4 Formulation of regular Lagrangian equation
74(1)
4.4.1 Sensitivity analysis
75(1)
4.5 Recursive Lagrangian equations
75(6)
4.5.1 Forward recursive kinematics
76(1)
4.5.2 Backward recursive dynamics
76(1)
4.5.3 Sensitivity analysis
77(1)
4.5.4 Kinematics sensitivity analysis
77(1)
4.5.5 Dynamics sensitivity analysis
78(2)
4.5.6 Joint profile discretization
80(1)
4.6 Examples using a 2-DOF arm
81(6)
4.6.1 The DH parameters
82(1)
4.6.2 Forward recursive kinematics
83(1)
4.6.3 Backward recursive dynamics
84(1)
4.6.4 Gradients
84(2)
4.6.5 Closed-form equations of motion
86(1)
4.7 Trajectory planning example
87(1)
4.8 Arm lifting motion with load example
88(2)
4.9 Concluding remarks
90(5)
References
92(3)
Chapter 5 Predictive Dynamics
95(32)
5.1 Introduction
95(1)
5.2 Problem formulation
95(4)
5.3 Dynamic stability: zero-moment point
99(2)
5.4 Performance measures
101(1)
5.5 Inner optimization
102(1)
5.6 Constraints
103(2)
5.6.1 Feasible set
104(1)
5.6.2 Minimal set of constraints
104(1)
5.7 Types of constraints
105(3)
5.7.1 Time-dependent constraints
105(2)
5.7.2 Time-independent constraints
107(1)
5.8 Discretization and scaling
108(1)
5.9 Numerical example: single pendulum
109(11)
5.9.1 Description of the problem
109(2)
5.9.2 Simple swing motion with boundary conditions---PD solution
111(3)
5.9.3 Oscillating motion with boundary conditions---PD solution
114(2)
5.9.4 Oscillating motion with boundary conditions and one state-response constraint---PD solution
116(2)
5.9.5 Oscillating motion with boundary conditions and two state-response constraints
118(2)
5.10 Example formulations
120(1)
5.11 Concluding remarks
120(7)
References
125(2)
Chapter 6 Strength and Fatigue: Experiments and Modeling
127(22)
6.1 Joint space
127(1)
6.2 Strength influences
128(4)
6.3 Strength assessment
132(2)
6.4 Normative strength data
134(3)
6.5 Representing strength percentiles
137(1)
6.6 Mapping strength to digital humans: strength surfaces
138(2)
6.7 Fatigue
140(5)
6.8 Strength and fatigue interaction
145(1)
6.9 Concluding remarks
145(4)
References
145(4)
Chapter 7 Predicting the Biomechanics of Walking
149(38)
7.1 Introduction
149(2)
7.2 Joints as degrees of freedom (DOF)
151(1)
7.3 Muscle versus joint space
151(1)
7.4 Spatial kinematics model
152(4)
7.4.1 A kinematic 55-DOF human model
152(2)
7.4.2 Global DOFs and virtual joints
154(1)
7.4.3 Forward recursive kinematics
155(1)
7.5 Dynamics formulation
156(2)
7.5.1 Backward recursive dynamics
156(1)
7.5.2 Sensitivity analysis
157(1)
7.5.3 Mass and inertia property
157(1)
7.6 Gait model
158(3)
7.6.1 One-step gait model
158(1)
7.6.2 Ground reaction forces (GRF)
159(2)
7.7 Zero-Moment point (ZMP)
161(3)
7.7.1 Global forces at the pelvis
162(1)
7.7.2 Global forces at origin
163(1)
7.7.3 ZMP calculation
163(1)
7.8 Calculating ground reaction forces (GRF)
164(2)
7.9 Optimization formulation
166(5)
7.9.1 Design variables
166(1)
7.9.2 Objective function
166(1)
7.9.3 Constraints
167(4)
7.10 Numerical discretization
171(1)
7.11 Example: predicting the gait
172(4)
7.11.1 Normal walking
172(4)
7.12 Cause and effect
176(7)
7.13 Implementations of the predictive dynamics walking formulation
183(1)
7.13.1 Effect of constrained joints
183(1)
7.13.2 Sideways and backward walking
183(1)
7.13.3 Effect of changing anthropometry
183(1)
7.13.4 Effect of changing loads
183(1)
7.13.5 Walking on uneven terrains
184(1)
7.13.6 Asymmetric walking
184(1)
7.13.7 Walking on different terrain types
184(1)
7.14 Concluding remarks
184(3)
References
185(2)
Chapter 8 Predictive Dynamics: Lifting
187(20)
8.1 Human skeletal model
187(1)
8.2 Equations of motion and sensitivities
187(3)
8.2.1 Forward recursive kinematics
187(2)
8.2.2 Backward recursive dynamics
189(1)
8.2.3 Sensitivity analysis
189(1)
8.3 Dynamic stability and ground reaction forces (GRF)
190(1)
8.4 Formulation
191(1)
8.4.1 Lifting task
191(1)
8.5 Predictive dynamics optimization formulation
192(5)
8.5.1 Design variables and time discretization
193(1)
8.5.2 Objective functions
194(1)
8.5.3 Constraints
194(3)
8.6 Computational procedure for multi-objective optimization
197(2)
8.6.1 Lifting determinants and error quantification
198(1)
8.7 Predictive dynamics simulation
199(2)
8.8 Validation
201(3)
8.9 Concluding remarks
204(3)
References
204(3)
Chapter 9 Validation of Predictive Dynamics Tasks
207(30)
9.1 Introduction
207(2)
9.2 Motion determinants
209(1)
9.3 Motion capture systems
209(4)
9.3.1 Overview
209(1)
9.3.2 Optical motion capture systems
210(1)
9.3.3 Marker placement protocol
211(1)
9.3.4 Subject preparation and data collection
212(1)
9.4 Methods
213(3)
9.4.1 Normalizing the data
213(1)
9.4.2 Validation methodology
214(2)
9.5 Validation of predictive walking task
216(8)
9.5.1 Walking task description
216(1)
9.5.2 Walking determinants
217(1)
9.5.3 Participants
217(1)
9.5.4 Results
217(7)
9.6 Validation of box-lifting task
224(9)
9.6.1 Lifting task description
224(1)
9.6.2 Box-lifting determinants
225(1)
9.6.3 Participants
225(1)
9.6.4 Results
225(8)
9.7 Feedback to the simulation
233(1)
9.8 Concluding remarks
233(4)
References
234(3)
Chapter 10 Concluding Remarks
237(10)
10.1 Benefits of predictive dynamics
237(3)
10.1.1 Using the Denavit-Hartenberg (DH) method is effective in modeling human kinematics
237(1)
10.1.2 Predictive dynamics solves dynamics without integration
238(1)
10.1.3 Predictive dynamics renders natural motion
238(1)
10.1.4 Predictive dynamics induces natural behavior
238(1)
10.1.5 Predictive dynamics admits cause and effect
238(1)
10.1.6 Predictive dynamics uses joint space, not muscle space
239(1)
10.1.7 Predictive dynamics uses dynamic strength surfaces
239(1)
10.1.8 The PD validation process is effective
240(1)
10.2 Applications
240(3)
10.2.1 Ergonomics
240(1)
10.2.2 Simulating an injury or a disability
240(1)
10.2.3 Sports biomechanics and kinesiology
241(1)
10.2.4 Human performance
241(1)
10.2.5 Testing equipment, digital prototyping, human systems integration
241(1)
10.2.6 Egress/ingress
242(1)
10.2.7 Unsafe situations
242(1)
10.3 Future research
243(4)
10.3.1 Soft-tissue dynamics
243(1)
10.3.2 Intelligence
243(1)
10.3.3 Psychological and physiological factors
243(1)
10.3.4 Modeling with a high level of fidelity
244(1)
10.3.5 Real-time simulation
244(1)
Reference
245(2)
Bibliography 247(22)
Index 269
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). Dr. Arora is the F. Wendell Miller Distinguished Professor, Emeritus, of Civil, Environmental and Mechanical Engineering at the University of Iowa. He was also Director of the Optimal Design Laboratory and Associate Director of the Center for Computer Aided Design. He is an internationally recognized expert in the fields of optimization, numerical analysis, and real-time implementation. His research interests include optimization-based digital human modeling, dynamic response optimization, optimal control of systems, design sensitivity analysis and optimization of nonlinear systems, and parallel optimization algorithms. Dr. Arora has authored two books, co-authored or edited five others, written 160 journal articles, 27 book chapters, 130 conference papers, and more than 300 technical reports.
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