Издательство Idea Group, 2006, -411 pp.Studies into human movement sciences have been usually undertaken from an interdisciplinary perspective. Individuals and groups who are involved in movement science research come from a number of diverse backgrounds, including: biomechanics, biomedical engineering, health science, exercise science, sports science, computer science, clinical science, physiotherapy, prosthetics and orthotics, to name a few. Research and development in movement sciences are progressing quite rapidly. The main aims of these advances are to gain a better understanding of the normal and abnormal human movement characteristics, and also to develop new and innovative ways of combating the rising health care costs around the globe. Analysis of gait and other human movements has proved very useful in revealing many useful insights into the recognition and assessment of movement abnormalities. In recent times, gait analysis is taken almost as a routine procedure in aiding many diagnostic and rehabilitative procedures. Common application examples include: the design of a rehabilitation program to assist the disabled, the planning and assessment of surgical outcomes, the recognition of gaits due to falls-risk in the elderly and also for the improvement of sports techniques and performance.Computational intelligence (CI) encompasses approaches primarily based on artificial neural networks, fuzzy logic rules, evolutionary algorithms and support vector machines. These methods have been applied to solve many complex and diverse problems. Recent years have seen many new developments in CI 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, CI 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 modelling. The contents of this book cover a wide range of relevant applications in human movement sciences written by leading researchers and academicians in the area.Section I: Methods and Tools for Movement AnalysisOverview of Movement Analysis and Gait FeaturesInertial Sensing in Biomechanics: A Survey of Computational Techniques Bridging Motion Analysis and Personal NavigationMonitoring Human Movement with Body-Fixed Sensors and its Clinical ApplicationsComputational Intelligence TechniquesSection II: Advances in Gait Analysis and ModellingModelling of Some Aspects of Skilled Locomotor Behaviour Using Artificial Neural NetworksVisualisation of Clinical Gait Data Using a Self-Organising Artificial Neural NetworkNeural Network Models for Estimation of Balance Control, Detection of Imbalance, and Estimation of Falls RiskRecognition of Gait Patterns Using Support Vector MachinesSection III: Applications in Rehabilitation and SportControl of Man-Machine FES SystemsEvolutionary Methods for Analysis of Human MovementDynamic Pattern Recognition in Sport by Means of Artificial Neural NetworksSection IV: Computational Modelling for Predicting Movement ForcesEstimation of Muscle Forces About the Ankle During Gait in Healthy and Neurologically Impaired SubjectsComputational Modelling in Shoulder Biomechanics