Human Behavior Learning and Transfer 1st Edition by Yangsheng Xu, Ka Keung Lee 1000656837 9781420036978
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ISBN 10: 1000656837
ISBN 13: 9781420036978
Author: Yangsheng Xu, Ka Keung C. Lee
Bridging the gap between human-computer engineering and control engineering, Human Behavior Learning and Transfer delineates how to abstract human action and reaction skills into computational models. The authors include methods for modeling a variety of human action and reaction behaviors and explore processes for evaluating, optimizing, and trans
Table of contents:
1 Introduction
1.1 Motivation
1.2 Overview
2 Introduction to Human Reaction Skill Modeling
2.1 Motivation
2.2 Related work
2.2.1 Skill learning through exploration
2.2.2 Skill modeling from human data
2.2.3 Neural network learning
2.2.4 Locally weighted learning
3 Learning of Human Control Strategy: Continuous and Discontinuous
3.1 Experimental design
3.1.1 Motivation
3.1.2 Simulation environment
3.1.3 Model class
3.2 Cascade neural networks with Kalman filtering
3.2.1 Cascade neural networks
3.2.2 Node-decoupled extended Kalman filtering
3.3 HCS models: continuous control
3.3.1 Cascade with quickprop learning
3.3.2 Cascade with NDEKF learning
3.3.3 Analysis
3.4 HCS models: discontinuous control
3.4.1 Hybrid continuous/discontinuous control
3.4.2 Experimental results
3.4.3 Analysis
4 Validation of Human Control Strategy Models
4.1 Need for model validation
4.2 Stochastic similarity measure
4.2.1 Hidden Markov models
4.2.2 Similarity measure
4.2.3 Properties
4.2.4 Distance measure
4.2.5 Data preprocessing
4.2.6 Vector quantization
4.2.7 Discretization compensation
4.2.8 HMM training
4.3 Human-to-model comparisons
5 Evaluation of Human Control Strategy
5.1 Introduction
5.2 Obstacle avoidance
5.2.1 Virtual path equivalence
5.2.2 Lateral offset estimation
5.2.3 Obstacle avoidance threshold
5.2.4 Obstacle avoidance velocity loss
5.3 Tight turning
5.3.1 Tight turning connections
5.3.2 Threshold with tight angle
5.4 Transient response
5.5 Time delay
5.6 Passenger comfort
5.7 Driving smoothness
5.8 Summary
6 Performance Optimization of Human Control Strategy
6.1 Introduction
6.2 Simultaneously perturbed stochastic approximation
6.3 Iterative optimization algorithm
6.4 Model optimization and performance analysis
6.5 Summary
7 Transfer of Human Control Strategy
7.1 Introduction
7.2 Model transfer based on similarity measure
7.2.1 Structure learning
7.2.2 Parameter learning
7.2.3 Experimental study
7.3 Model compensation
7.4 Summary
8 Transferring Human Navigational Skills to Smart Wheelchair
8.1 Introduction
8.1.1 Related work
8.2 Methodology
8.2.1 Problem formulation
8.2.2 Theoretical foundation
8.3 Experimental study
8.3.1 Settings
8.3.2 Experiment 1: Navigation
8.3.3 Experiment 2: Localization
8.4 Analysis
8.4.1 Performance evaluation
8.4.2 Advantages of the approach
8.4.3 Choices of sensor-configuration mapping
8.4.4 Generalization of the study
8.5 Conclusion
9 Introduction to Human Action Skill Modeling
9.1 Learning action models
9.2 Dimension reduction formulation
9.3 Related research
10 Global Parametric Methods for Dimension Reduction
10.1 Introduction
10.2 Parametric methods for global modeling
10.2.1 Polynomial regression
10.2.2 First principal component
10.3 An experimental data set
10.4 PCA for modeling performance data
10.5 NLPCA
10.6 SNLPCA
10.7 Comparison
10.8 Characterizing NLPCA mappings
11 Local Methods for Dimension Reduction
11.1 Introduction
11.2 Non-parametric methods for trajectory fitting
11.3 Scatter plot smoothing
11.4 Action recognition using smoothing splines
11.5 An experiment using spline smoothing
11.6 Principal curves
11.6.1 Definition of principal curves
11.6.2 Distance property
11.6.3 Principal curves algorithm for distributions
11.6.4 PC for data sets: projection
11.6.5 PC for data sets: conditional expectation
11.7 Expanding the one-dimensional representation
11.8 Branching
11.9 Over-fitting
12 A Spline Smoother in Phase Space for Trajectory Fitting
12.1 Smoothing with velocity information
12.2 Problem formulation
12.3 Solution
12.4 Notes on computation and complexity
12.5 Similar parameterizations
12.6 Multi-dimensional smoothing
12.7 Estimation of variances
12.8 Windowing variance estimates
12.9 The effect of velocity information
12.10 Cross-validation
13 Analysis of Human Walking Trajectories for Surveillance
13.1 Introduction
13.2 System overview
13.3 Background subtraction
13.4 Local trajectory point classification
13.5 Global trajectory similarity estimation
13.6 Trajectory normality classifier
13.7 Experiment 1: Trajectory normality classifier
13.8 Further analysis on global trajectory similarity based on LCSS
13.9 Methodology used in boundary modeling
13.9.1 Trajectory similarity based on LCSS
13.10 LCSS boundary limit establishment
13.10.1 LCSS thresholds learnt from support vector regression
13.10.2 LCSS thresholds learnt from cascade neural networks
13.10.3 Fixed LCSS thresholds
13.10.4 Variable LCSS thresholds
13.11 Experiment 2: Boundary modeling
13.12 Discussion
13.13 Conclusion
14 Modeling of Facial and Full-Body Actions
14.1 Facial expression intensity modeling
14.1.1 Related work
14.1.2 System overview
14.1.3 Extraction of facial motion data
14.1.4 Automatic extraction of facial expression intensity from transition
14.1.5 The learning of facial expression intensity
14.1.6 Experiment
14.1.7 Discussion
14.1.8 Conclusion
14.2 Full-body action modeling
14.2.1 System overview
14.2.2 Feature extraction
14.2.3 Learning system based on support vector classification
14.2.4 Experiment 1: Recognition of motions of table tennis players
14.2.5 Experiment 2: Detection of fighting actions
14.2.6 Discussion
14.2.7 Conclusion
15 Conclusions
A Appendix A: Human Control Data
A.1 Larry
A.2 Curly
A.3 Moe
A.4 Groucho
A.5 Harpo
A.6 Zeppo
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Tags: Yangsheng Xu, Ka Keung Lee, Behavior, Human