The development of a gait waveform classification tool

步态波形分类工具的开发

• 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: 2009
• 资助国家: 加拿大
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=427438
• 关键词: development; gait; waveform; classification; tool

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力学。生物力学家使用最先进的运动捕捉系统来记录步态模式并描述运动的力学。步态数据给研究人员带来了许多挑战，因为它包含大量难以分析和比较的波形。尚未开发出公认的步态曲线比较方法。因此，许多研究人员从波形中提取离散点（峰值），而忽略了有价值的时间信息和运动模式。需要基于整个波形对步态模式进行分类和比较的数值工具。 这项研究的目的是进一步我的工作的设计和实现的算法，减少大量的机械数据的一系列一维指数的正常的年龄匹配的规范性数据的基础上。指数或分类器使用自动系统（运动捕获和随后的数据分析）来基于幅度、运动模式和步态曲线之间的相关性检测运动异常。输出是一系列的数字分数，使机械和生理意义，并产生可解释的结果。很少有分类器存在，可以满足结合相关的多维输入数据，并提供易于解释的输出的双重约束。拟议的工作是新颖的，因为它结合了机械数据，肌电信号，和功能的措施。到目前为止，没有其他分类器包括所有这些措施，这被认为是一个典型的步态分析的组成部分。分类器的开发将为研究人员提供一种自动化工具，以减少机械和肌电数据，并量化步态波形的差异。这将导致更好地了解人群或个人之间步态模式的差异。