The development of a gait waveform classifier using electromyograpic signals

使用肌电信号的步态波形分类器的开发

• 批准号: 375086-2009
• 负责人: Chester, Victoria
• 金额: $ 3.77万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments - Category 1 (<$150,000)
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=412078
• 关键词: development; gait; waveform; classifier; using

Walking, or gait, is a fundamental human motion. The loss or alteration of a person's walking skills greatly affects one's ability to function and quality of life. Biomechanists use state of the art motion capture systems to record gait patterns and describe the mechanics of normal and abnormal movements (gait analysis). Quantitative gait analyses typically examine kinematic, kinetic, and electromyographic data. The analysis of this mechanical and myoelectric data is challenging due to the high-dimensionality, temporal dependence, high variability, and highly correlated nature of the data. As a result, comparison of gait curves (i.e. normal vs. abnormal) is very difficult. To address the challenges associated with the large volumes of mechanical data, researchers have developed various data reduction and classification techniques. However, most are limited in either their ability to detect abnormal movement patterns and/or their interpretability. 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 waveform data, multisegment foot kinematics, and myoelectric wavelet data. To date, no other classifiers include all of these important measures. The development of the classifier will provide researchers with an automated tool to reduce mechanical and myoelectric data and quantify differences in gait waveforms. The gait classifier will also identify which measures are the best discriminators of abnormal and normal motion. This will lead to a greater understanding of the differences in gait patterns between populations or individuals.

步行或步态是人类的基本运动。 一个人的步行技巧的损失或改变会极大地影响人们的运作能力和生活质量。生物力学使用艺术运动捕获系统的状态来记录步态模式并描述正常运动和异常运动的力学（步态分析）。 定量步态分析通常检查运动学，动力学和肌电图数据。 由于数据的高差异，时间依赖性，高变异性和高度相关性，对这种机械和肌电数据的分析是具有挑战性的。 结果，比较步态曲线（即正常与异常）非常困难。为了解决与大量机械数据相关的挑战，研究人员开发了各种数据降低和分类技术。 但是，大多数人在检测异常运动模式和/或其解释性的能力上受到限制。 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，因为它结合了机械波形数据，多段脚动力学和肌电小波数据。 迄今为止，还没有其他分类器包括所有这些重要措施。 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。 步态分类器还将确定哪些度量是异常和正常运动的最佳歧视者。 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。