QUANTITATIVE GAIT ANALYSIS

定量步态分析

• 批准号: 3156995
• 负责人: MURALI KADABA
• 金额: $ 6.44万
• 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 1987-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3156995
• 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、力进行定量步态分析 板和肌电图在步态评估中具有公认的价值 残疾和治疗的定量评估。 但是，尽管 技术的巨大进步，步态分析仍然缺乏广泛的应用 由于数据选择的不确定性，临床实用性， 操纵和分析。 由于数据采集通常仅限于 多个步态周期的单次测试，涉及一个重要问题 选择一个步态周期进行分析，该步态周期代表 患者的步态及其可靠性的确定作为临床的基础 决策。 另一个问题涉及大数据的操纵 通过测量各种动力学、运动学产生的大量数据 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。 这个过程进一步 由于患者的步态模式各不相同，这一事实变得更加复杂 相同的综合症，甚至在正常人之间也有一定程度的差异。 通过使用统计模式识别可以简化解释 技术，但要使模式识别方法取得成功， 大量数据必须简化为一组简约的特征 准确地描述步态模式。 此外，代表 与各种步态参数相关的图形模式 离散变量集将使数值比较更有意义。 本研究的第一部分将调查可重复性和临床 所选动力学、运动学和肌电图参数的可靠性 通过对正常受试者和两个受试者的重复步态分析获得 骨科患者的代表性群体，使用统计数据 标准（方差比）。 使用方差比作为的可行性 平均步态周期以产生代表性周期的标准 将接受检查。 研究的第二部分将涉及 应用主成分分析来表示步态模式 以简洁的方式，作为应用模式的第一步 识别技术。 派生特征的有效性 代表步态异常将使用相同的正常和 骨科患者受试者。 预计调查结果 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来进行选择和分析 评估原始步态数据的可靠性，并通过定义技术 可有效用于模式识别的特征选择 技术。