QUANTITATIVE GAIT ANALYSIS

定量步态分析

• 批准号: 3156996
• 负责人: MURALI KADABA
• 金额: $ 6.42万
• 依托单位: NEW YORK STATE DEPARTMENT OF HEALTH
• 依托单位国家: 美国
• 财政年份: 1985
• 资助国家: 美国
• 起止时间: 1985-01-01 至 1989-01-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3156996
• 关键词: arthritis; artificial intelligence; cerebral palsy; chordate locomotion; computer assisted diagnosis; computer graphics /printing; electromyography; hip; human subject; musculoskeletal disorder diagnosis; orthopedics

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.

使用计算机辅助视频运动分析的定量步态分析，力 板和肌电图是公认的价值评估步态 残疾和治疗的定量评估。 但尽管 虽然技术上有了巨大的进步，但步态分析仍然缺乏广泛的 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 在几个步态周期的单一测试会话，一个重要的问题是， 选择步态周期进行分析， 患者的步态和确定其可靠性作为临床的基础 决策。 另一个问题涉及对大规模 通过测量各种动力学、运动学、 和几个步态周期的肌电图参数来检测 具有临床意义的表现模式。 该方法进一步 复杂的是，患有这种疾病的患者的步态模式各不相同， 相同的症状，在一定程度上，甚至在正常人之间也有差异。 使用统计模式识别可以简化解释 技术，但对于模式识别方法要成功， 大量的数据必须简化为一组简单的特征 准确描述步态模式。 此外，代表 与各种步态参数相关联的图形模式， 离散的变量集将使数值比较更有意义。 本研究的第一部分将研究重复性和临床 所选动力学、运动学和肌电图参数的可靠性 从正常受试者的重复步态分析中获得， 骨科患者的代表性群体，使用统计学 方差比（Variance Ratio） 使用方差比作为 用于平均步态周期以产生代表性周期的标准还 将被审查。 研究报告的第二部分将讨论 主成分分析在步态模式表示中的应用 作为应用模式的第一步， 识别技术 衍生特征的有效性， 将使用相同的正常和 骨科患者受试者。 预计， 拟议的研究将提高定量步态的临床有效性 通过定义实用的计算机算法进行分析， 评估原始步态数据的可靠性，并通过定义 特征选择可以有效地用于模式识别 技术.

项目成果

Dynamic electromyography. I. Numerical representation using principal component analysis.

动态肌电图。

• DOI: 10.1002/jor.1100080214
• 发表时间: 1990
• 期刊: Journal of orthopaedic research : official publication of the Orthopaedic Research Society
• 作者: Wootten,ME;Kadaba,MP;Cochran,GV
• 通讯作者: Cochran,GV