Central regulation system

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Table of contents

1 GENERAL INTRODUCTION 
2 THE HUMAN POSTURAL STABILITY ANALYSIS 
2.1 Introduction
2.2 The human postural system
2.2.1 Denition
2.2.2 The main components of the postural system
2.2.2.1 Ocular sensor
2.2.2.2 Vestibular system
2.2.2.3 The base of support : the foot
2.2.2.4 Central regulation system
2.2.2.5 Motor response
2.2.3 The primary strategies to maintain stability
2.3 Tools for evaluating postural stability
2.3.1 Postural recording systems
2.3.1.1 Video-based methods
2.3.1.2 Body-worn inertial sensors-based methods
2.3.1.3 Force platform-bsed methods : the stabilometer
2.3.2 Protocols for COP displacements recordings
2.3.3 Clinical stabilometry standardization
2.4 Postural stability analysis techniques
2.5 Conclusion
3 EMD-BASED APPROACH FOR POSTURE ANALYSIS 
3.1 Introduction
3.2 Stabilometric data acquisition protocol
3.3 Empirical Mode Decomposition and its variant Ensemble Empirical Mode decomposition
3.3.1 EMD basics
3.3.2 Sifting process
3.3.3 Stopping criteria
3.3.4 Ensemble Empirical Model Decomposition
3.4 Stabilogram-diusion analysis
3.4.1 Brownian motion
3.4.2 Mean square displacement
3.5 EMD-based approach for posture analysis
3.5.1 Diusion curves modeling
3.6 Results and discussions
3.6.1 Balance analysis : classical approach
3.6.2 Balance analysis using EMD
3.6.2.1 Gain analysis
3.6.2.2 CP analysis
3.7 Conclusion
4 EMD-BASED FEATURE EXTRACTION AND SELECTION FOR SUBJECTS CLASSIFICATION 
4.1 Introduction
4.2 Classication techniques
4.2.1 K Nearest Neighbors
4.2.2 Classication and regression tree (CART)
4.2.3 Random Forest
4.2.4 Support Vector Machine
4.3 Feature extraction and selection for subjects classication
4.3.1 Feature extraction
4.4 Experimental results
4.4.1 Performance evaluation
4.4.2 Results and discussions
4.4.2.1 Obtained results using data collected from all conditions
4.4.2.2 Obtained results using data collected from each condition (IMFs data)
4.4.2.3 Obtained results using data collected from each condition (Raw data)
4.5 Conclusion
5 HMM-BASED CLASSIFICATION APPROACH 
5.1 Introduction
5.2 Hidden Markov Models
5.2.1 Introduction
5.2.2 Markov Chain
5.2.3 Discrete HMM
5.2.4 Gaussian HMM
5.3 HMM-based classication approach
5.4 Results and discussions
5.5 Conclusion
6 HMMREGRESSION-BASED APPROACH FOR AUTOMATIC SEG- MENTATION OF STABILOMETRIC SIGNALS 
6.1 Introduction
6.2 Hidden Markov Model Regression
6.2.1 Simple Hidden Markov Model Regression
6.2.1.1 Parameter estimation
6.2.2 Multiple Hidden Markov Model Regression
6.2.2.1 Parameter estimation
6.3 HMM Regression-based approach for automatic segmentation of stabilometric signals
6.4 Results and discussions
6.4.1 Segmentation based on feet and visual conditions of healthy subjects
6.4.2 Segmentation based on feet and visual conditions of PD subjects .
6.5 Conclusion
7 GENERAL CONCLUSION AND PERSPECTIVES 
7.1 Conclusion
7.2 Perspectives
Bibliographie