Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, 1st Edition

  • Published By:
  • ISBN-10: 1591408385
  • ISBN-13: 9781591408383
  • DDC: 612.7
  • Grade Level Range: College Freshman - College Senior
  • 396 Pages | eBook
  • Original Copyright 2006 | Published/Released November 2006
  • This publication's content originally published in print form: 2006

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Recent years have seen many new developments in computational intelligence techniques and, consequently, this has led to an exponential increase in the number of applications in a variety of areas, including engineering, finance, social and biomedical. In particular, computational intelligence techniques are increasingly being used in biomedical and human movement areas because of the complexity of the biological systems as well as the limitations of the existing quantitative techniques in modeling. Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques contains information regarding state-of-the-art research outcomes and cutting-edge technology from leading scientists and researchers working on various aspects of the human movement. Readers of this book will gain an insight into this field as well as access to pertinent information, which they will be able to use for continuing research in this area.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
IGP Forthcoming Titles in the Computational Intelligence and Its Applications Series.
Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques Table of Contents.
1: Methods and Tools for Movement Analysis.
2: Overview of Movement Analysis and Gait Features.
3: Inertial Sensing in Biomechanics: A Survey of Computational Techniques Bridging Motion Analysis and Personal Navigation.
4: Monitoring Human Movement with Body-Fixed Sensors and Its Clinical Applications.
5: Computational Intelligence Techniques.
6: Advances in Gait Analysis and Modelling.
7: Modelling of Some Aspects of Skilled Locomotor Behaviour Using Artificial Neural Networks.
8: Visualisation of Clinical Gait Data Using a Self-Organising Artificial Neural Network.
9: Neural Network Models for Estimation of Balance Control, Detection of Imbalance, and Estimation of Falls Risk.
10: Recognition of Gait Patterns Using Support Vector Machines.
11: Applications in Rehabilitation and Sport.
12: Control of Man-Machine FES Systems.
13: Evolutionary Methods for Analysis of Human Movement.
14: Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks.
15: Computational Modelling for Predicting Movement Forces.
16: Estimation of Muscle Forces About the Ankle During Gait in Healthy and Neurologically Impaired Subjects.
17: Computational Modelling in Shoulder Biomechanics.
About the Authors.