Quantitative Characterization of Complex Motion Patterns Using Shape-based and Multivariate Techniques

使用基于形状和多元技术的复杂运动模式的定量表征

• 批准号: 0727083
• 负责人: Elizabeth Hsiao-Wecksler
• 金额: --
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0727083&HistoricalAwards=false
• 关键词: Quantitative; Characterization; Complex; Motion; Patterns

The characterization of complex motion patterns in multisegmented biological organisms is typically achieved by the identification and measurement of task-related behaviors and the assessment of deviations from these normative behaviors. The basic hypothesis of this proposal is that there are systematic and quantifiable relationships between observed deviations in motion patterns and underlying physiological limitations. Currently available tools are largely unable to resolve these relationships as they primarily examine discrete events during a specific motion or are based on univariate statistical techniques. Thus, they fall short in quantifying spatiotemporally complex motion patterns and in detecting interactions across multiple segments and joints.The fundamental objective of this project is to establish a diagnostic, multivariate technique for characterizing complex motion patterns and correlating specific motion patterns with physiological conditions. Specifically, the proposed research will: (i) create an "Integrated Multivariate Motion Analysis" computational tool that combines shape-based analysis techniques with multivariate statistical tools to allow for improved quantification of complex motion patterns; (ii) benchmark the statistical technique against a library of task-specific lower-limb motion patterns generated using numerical optimization techniques applied to a simple mechanical model of the lower limb with unconstrained and constrained joint mobility; and (iii) establish the degree to which the statistical technique is able to identify the presence and degree of constraint in a set of controlled, experimental motion-captured data of human walking without and with braces that artificially constrain the movements at the knee or ankle. We expect that a successful outcome of the proposed effort will transform studies of gait and other complex motions. The tools developed from this project will significantly advance diagnostic capabilities, aid in the evaluation and treatment of movement conditions, and permit more accurate and comprehensive comparisons of segmental movements in a variety of taxa. These tools will lead to novel inferences about the complexity, performance, efficiency and health of biological and mechanical systems. This project also provides a multidisciplinary research and educational environment for faculty, graduate, and undergraduate students in engineering, anthropology, and psychology with interests in movement analysis, computational simulation of dynamical systems, and the statistical comparison of complex shapes at both the University of Illinois and Stockton College of New Jersey

多节段生物有机体中复杂运动模式的表征通常通过识别和测量任务相关行为以及评估与这些规范行为的偏差来实现。这个提议的基本假设是，在观察到的运动模式的偏差和潜在的生理限制之间存在系统的和可量化的关系。目前可用的工具在很大程度上无法解决这些关系，因为它们主要检查特定运动期间的离散事件或基于单变量统计技术。因此，他们在量化时空复杂的运动模式，并在检测跨多个段和joints.The的基本目标是建立一个诊断，多变量的技术特征复杂的运动模式和相关的特定运动模式与生理条件的相互作用不足。具体地说，所提出的研究将：（i）创建一个“集成多变量运动分析”计算工具，该工具将基于形状的分析技术与多变量统计工具相结合，以允许改进复杂运动模式的量化;（ii）将统计技术与一个特定任务的较低-使用数值优化技术生成的肢体运动模式，应用于具有无约束和约束关节活动性的下肢的简单力学模型;以及（iii）建立统计技术能够在不带和带人工约束膝盖或脚踝处的运动的支架的情况下识别人类行走的一组受控的实验运动捕获数据中约束的存在和程度的程度。我们期望，所提出的努力的成功结果将改变步态和其他复杂运动的研究。从这个项目开发的工具将显着提高诊断能力，在运动条件的评估和治疗援助，并允许更准确和全面的比较节段运动在各种类群。 这些工具将导致新的推论的复杂性，性能，效率和健康的生物和机械系统。该项目还为伊利诺伊大学和新泽西的斯托克顿学院的工程、人类学和心理学的教师、研究生和本科生提供了一个多学科的研究和教育环境，他们对运动分析、动力系统的计算模拟以及复杂形状的统计比较感兴趣