The development of a gait waveform classification tool

步态波形分类工具的开发

基本信息

批准号：
298182-2008
负责人：
Chester, Victoria
金额：
$ 1.31万
依托单位：
University of New Brunswick
依托单位国家：
加拿大
项目类别：
Discovery Grants Program - Individual
财政年份：
2012
资助国家：
加拿大
起止时间：
2012-01-01 至 2013-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=504817
关键词：
development gait waveform classification tool

项目摘要

Gait analysis aims to quantify and assess the mechanics of normal and abnormal movement patterns during walking. Biomechanists use state of the art motion capture systems to record gait patterns and describe the mechanics of movement. Gait data provides many challenges to researchers as it consists of vast numbers of waveforms that are difficult to analyze and compare. An accepted method of gait curve comparison has not been developed. As a result, many researchers extract discrete points (peaks) from the waveforms and ignore the valuable temporal information and patterns of motion. Numerical tools that classify and compare gait patterns based on the entire waveform are needed. This study aims to further my work on the design and implementation of algorithms that reduce the vast quantities of mechanical data to a series of one-dimensional indices of normality based on age-matched normative data. The indices, or classifiers, use an automatic system (motion capture and subsequent data analysis) to detect movement abnormalities based on magnitude, pattern of motion, and correlations between gait curves. The output is a series of numerical scores that make mechanical and physiological sense and yield interpretable results. Very few classifiers exist that can satisfy the two-fold constraint of incorporating correlated multi-dimensional input data and providing readily interpretable output. The proposed work is novel in that it incorporates mechanical data, myoelectric signals, and functional measures. To date, no other classifiers include all of these measures, which are considered integral to a typical gait analysis. The development of the classifier will provide researchers with an automated tool to reduce mechanical and myoelectric data and quantify differences in gait waveforms. This will lead to a greater understanding of the differences in gait patterns between populations or individuals.

步态分析旨在量化和评估步行过程中正常和异常运动模式的机制。生物力学使用最先进的运动捕获系统来记录步态模式并描述运动的机制。步态数据为研究人员提供了许多挑战，因为它由难以分析和比较的大量波形组成。尚未开发一种接受步态曲线比较的方法。结果，许多研究人员从波形中提取离散点（峰），而忽略了有价值的时间信息和运动模式。需要根据整个波形对步态模式进行分类和比较的数值工具。这项研究旨在进一步进一步研究算法的设计和实施，这些算法将大量的机械数据减少到基于年龄匹配的规范性数据的一系列正态性指数。这些指数或分类器使用自动系统（运动捕获和后续数据分析）来检测基于步态曲线之间的大小，运动模式和相关性的运动异常。输出是一系列数值得分，它们具有机械和生理意义，并产生可解释的结果。很少有分类器能够满足结合相关的多维输入数据并提供易于解释的输出的两倍约束。拟议的工作是新颖的，因为它结合了机械数据，肌电信号和功能度量。迄今为止，还没有其他分类器包括所有这些措施，这些措施被认为是典型步态分析不可或缺的一部分。分类器的开发将为研究人员提供自动化工具，以减少机械和肌电数据并量化步态波形的差异。这将使人们对人群或个人之间的步态模式差异有更深入的了解。