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 Analysis
Overview of Movement Analysis and Gait Features
Inertial Sensing in Biomechanics: A Survey of Computational Techniques Bridging Motion Analysis and Personal Navigation
Monitoring Human Movement with Body-Fixed Sensors and its Clinical Applications
Computational Intelligence Techniques
Section II: Advances in Gait Analysis and Modelling
Modelling of Some Aspects of Skilled Locomotor Behaviour Using Artificial Neural Networks
Visualisation of Clinical Gait Data Using a Self-Organising Artificial Neural Network
Neural Network Models for Estimation of Balance Control, Detection of Imbalance, and Estimation of Falls Risk
Recognition of Gait Patterns Using Support Vector Machines
Section III: Applications in Rehabilitation and Sport
Control of Man-Machine FES Systems
Evolutionary Methods for Analysis of Human Movement
Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks
Section IV: Computational Modelling for Predicting Movement Forces
Estimation of Muscle Forces About the Ankle During Gait in Healthy and Neurologically Impaired Subjects

Computational Modelling in Shoulder Biomechanics

Издательство Idea Group, 2006, -411 pp.对人类运动科学的研究通常从跨学科的角度进行。参与运动科学研究的个人和团体来自许多不同的背景，包括：生物力学、生物医学工程、健康科学、运动科学、体育科学、计算机科学、临床科学、物理疗法、假肢和矫形器，等等。运动科学的研究和发展进展相当迅速。这些进展的主要目的是为了更好地了解正常和不正常的人类运动特征，同时也是为了开发新的和创新的方法来应对全球范围内不断上升的医疗保健费用。对步态和其他人类运动的分析已被证明非常有用，揭示了许多关于识别和评估运动异常的有用见解。近来，步态分析几乎被当作辅助许多诊断和康复程序的常规程序。常见的应用实例包括：设计康复方案以帮助残疾人，规划和评估手术结果，识别老年人跌倒风险导致的步态，以及改善运动技术和表现。

计算智能（CI）包含了主要基于人工神经网络、模糊逻辑规则、进化算法和支持向量机的方法。这些方法已被应用于解决许多复杂和多样化的问题。近年来，CI技术有了许多新的发展，因此导致工程、金融、社会和生物医学等各种领域的应用数量呈指数级增长。特别是，由于生物系统的复杂性以及现有定量技术在建模方面的局限性，CI技术正越来越多地被用于生物医学和人类运动领域。本书的内容涵盖了人类运动科学中广泛的相关应用，由该领域的主要研究人员和院士撰写。第一节：运动分析的方法和工具

运动分析和步态特征概述

生物力学中的惯性感应。衔接运动分析和个人导航的计算技术调查

用身体固定的传感器监测人体运动及其临床应用

计算智能技术

第二部分。步态分析和建模的进展

使用人工神经网络对熟练运动行为的某些方面进行建模

使用自组织人工神经网络对临床步态数据进行可视化处理

估计平衡控制、检测失衡和估计跌倒风险的神经网络模型

使用支持向量机的步态模式识别

第三部分：康复和运动中的应用

人-机FES系统的控制

人类运动分析的进化方法

通过人工神经网络在运动中的动态模式识别

第四节：用于预测运动力的计算模型

健康和神经障碍受试者在步态中对踝关节的肌肉力量的估计

肩部生物力学的计算模型