Modern methods for affordable clinical gait analysis : theories and applications in healthcare systems /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Nandy, Anup (Autor)
Otros Autores: Chakraborty, Saikaat, Chakraborty, Jayeeta, Venture, Gentiane
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2021.
Temas:
Gait disorders.
Gait disorders > Diagnosis > Equipment and supplies.
Motion detectors.
Gait in humans.
Medical instruments and apparatus.
Gait Analysis
Gait
Equipment and Supplies
Troubles de la locomotion.
D�etecteurs de mouvement.
D�emarche.
M�edecine > Appareils et instruments.
Motion detectors
Gait disorders
Gait in humans
Medical instruments and apparatus
Lopen.
Bewegingsleer.
Diagnostiek.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • MODERN METHODS FOR AFFORDABLE CLINICAL GAIT ANALYSIS
  • MODERN METHODS FOR AFFORDABLE CLINICAL GAIT ANALYSIS
  • Copyright
  • Contents
  • About the authors
  • Preface
  • Acknowledgment
  • 1
  • Introduction
  • 1.1 What is gait?
  • 1.2 Gait cycle
  • 1.3 Features of gait
  • 1.3.1 Spatio-temporal
  • 1.3.2 Kinematic
  • 1.3.3 Kinetic
  • 1.3.4 Anthropometric
  • 1.3.5 Electromyography
  • 1.4 Model-based versus model-free gait assessment
  • 1.4.1 Model-based human gait analysis
  • 1.4.2 Model-free human gait analysis
  • 1.5 Applications of gait analysis
  • 1.6 Clinical aspects of human gait
  • 1.6.1 Gait signal segmentation
  • 1.6.2 Pathological gait detection
  • 1.6.3 Injury prevention and recovery prediction
  • 1.7 Sensors for gait data acquisition
  • 1.8 Summary
  • References
  • 2
  • Statistics and computational intelligence in clinical gait analysis
  • 2.1 Introduction
  • 2.2 Statistics in clinical gait data
  • 2.2.1 Confidence interval, p-value, and effect size
  • 2.2.2 Statistics in clinical trials
  • 2.2.3 Systematic review and meta-analysis
  • 2.3 Computational intelligence in clinical gait data
  • 2.3.1 Why computational intelligence is important?
  • 2.3.2 Learning paradigm
  • 2.3.3 Applications of computational intelligence in clinical gait data
  • 2.4 Statistics versus computational intelligence
  • 2.5 Summary
  • References
  • 3
  • Low-cost sensors for gait analysis
  • 3.1 Introduction
  • 3.2 Motion capture sensors for gait
  • 3.2.1 Classification of sensors
  • 3.2.2 Gold standard sensors
  • 3.2.3 Affordable sensors
  • 3.3 Microsoft kinect
  • 3.3.1 First and second generation
  • 3.3.2 Hardware and software specification
  • 3.3.3 Data streams of kinect
  • 3.3.4 Application in clinical gait assessment
  • 3.4 Wearable sensors
  • 3.4.1 Inertial sensors
  • 3.4.1.1 Types of inertial sensors
  • 3.4.1.2 Cost analysis of inertial sensors
  • 3.4.1.3 Applications of inertial sensor in clinical gait assessment
  • 3.4.2 Electromyography sensors
  • 3.4.2.1 MyoWare muscle sensor
  • 3.4.3 Others: force sensitive resistors, goniometers
  • 3.5 Summary
  • References
  • 4
  • Validation study of low-cost sensors
  • 4.1 Introduction
  • 4.2 Kinect validation for clinical usages
  • 4.3 Inertial sensor validation on estimating joint angles
  • 4.3.1 Evaluation metrics for validation of estimated joint angles
  • 4.4 Summary
  • References
  • 5
  • Gait segmentation and event detection techniques
  • 5.1 Introduction
  • 5.2 Why gait cycle segmentation?
  • 5.3 Vision sensor-based gait cycle segmentation
  • 5.3.1 Threshold-based methods
  • 5.3.2 Machine learning-based methods
  • 5.4 Kinect in gait cycle segmentation
  • 5.5 Inertial sensor-based gait segmentation
  • 5.5.1 Threshold-based methods
  • 5.5.2 Machine intelligence-based methods
  • 5.6 Electromyography sensor-based gait segmentation
  • 5.6.1 Statistical methods

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