The development of a gait waveform classifier using electromyograpic signals

行走或步态是人类的基本运动。 一个人的行走技能的丧失或改变极大地影响了一个人的功能和生活质量。生物力学家使用最先进的运动捕捉系统来记录步态模式，并描述正常和异常运动的力学（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。 由于数据的高维性、时间依赖性、高可变性和高度相关性，对这种机械和肌电数据的分析具有挑战性。 因此，步态曲线（即正常与异常）的比较非常困难。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简化和分类技术。 然而，大多数是有限的，无论是他们的能力，以检测异常运动模式和/或其可解释性。 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，它结合了机械波形数据，多段脚运动学，和肌电小波数据。 迄今为止，没有其他分类包括所有这些重要的措施。 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。 步态分类器还将识别哪些措施是异常和正常运动的最佳鉴别器。 这将导致更好地了解人群或个人之间步态模式的差异。

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.