QUANTITATIVE GAIT ANALYSIS

使用计算机辅助视频运动分析的定量步态分析，力量 平板和肌电图仪在步态评估中有公认的价值 在残疾评估和治疗的量化评估方面也是如此。但是，尽管 技术上的巨大进步，步态分析仍然缺乏广泛的 临床应用由于与数据选择相关的不确定性， 操纵和分析。由于数据采集通常仅限于 在几个步态周期中进行一次测试，一个至关重要的问题是 选择用于分析的步态周期，该步态周期代表 患者步态及其可靠性的测定作为临床依据 做决定。另一个问题与操纵大型 通过测量各种运动学、运动学 以及几个步态周期的肌电参数来检测 临床上有意义的表现模式。这个过程是进一步的 复杂的事实是，步态模式在不同的患有 相同的综合征，在一定程度上，即使在正常人群中也是不同的。 可以通过使用统计模式识别来简化解释 技术，但要想让模式识别方法取得成功， 必须将大量数据缩减为一组简约的要素 它们准确地描述了步态模式。此外，还可以表示 与各种步态参数相关联的图形图案 离散变量集将使数值比较更有意义。 这项研究的第一部分将调查重复性和临床。 选定的运动学、运动学和肌电参数的可靠性 通过对正常受试者和两名受试者的重复步态分析获得 具有代表性的骨科患者群体，使用统计方法 标准(方差率)。将方差比作为 用于平均步态周期以产生代表性周期的标准也 将会被检查。研究的第二部分将涉及 主成分分析在步态模式表示中的应用 以一种简洁的方式，作为应用模式的第一步 识别技术。派生功能的有效性在 表示步态异常的评估将使用相同的正常和 骨科患者研究对象。预计此次评选的结果 拟议的研究将提高量化步态的临床有效性 通过定义实用的计算机算法来分析选择和 评估原始步态数据的可靠性，并通过定义 一种可有效用于模式识别的特征选择 技巧。

Quantitative gait analysis using computer aided videomotion analysis, force plates and electromyography is of recognized value in assessment of gait disabilities and in quantitative evaluation of treatment. But, despite dramatic improvements in techniques, gait analysis still lacks widespread clinical utility because of uncertainties relating to data selection, manipulation, and analysis. Since data acquisition is usually limited to a single test session over several gait cycles, one vital problem concerns selection of a gait cycle for analysis that is representative of the patient's gait and determination of its reliability as a basis for clinical decision making. Another problem relates to the manipulation of large quantities of data generated by measurements of various kinetic, kinematic and electromyographic parameters over several gait cycles to detect clinically significant patterns of performance. This process is further complicated by the fact that gait patterns vary among patients with the same syndorme and to a certain extent, vary even among normals. Interpretration may be simplified by using statistical pattern recognition techniques, but for a pattern recognition approach to be successful, the enormous quantity of data must be reduced to a parsimonious set of features which describe gait patterns accurately. Furthermore, representation of graphic patterns associated with various gait parameters in terms of a discrete set of variables would make numerical comparison more meaningful. The first part of this study will investigate repeatability and clinical reliability of selected kinetic, kinematic and electromyographic parameters obtained from repeated gait analyses on normal subjects and two representative groups of orthopaedic patients, using a statistical criterion (variance ratio). The feasibility of using the variance ratio as a criterion for averaging gait cycles to yield a representative cycle also will be examined. The second part of the study will deal with the application of principal component analysis for representing gait patterns in a concise manner, as an initial step to application of pattern recognition techniques. The effectiveness of the derived features in representing gait abnormalities will be evaluated using the same normal and orthopaedic patient subjects. It is anticipated that the results of the proposed study will enhance the clinical effectiveness of quantitative gait analysis by defining practical computer algorithms for selecting and assessing reliability of raw gait data, and by defining techniques for feature selection that can be used effectively in pattern recognition techniques.