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是通过识别和测量与任务相关的行为以及评估与这些规范行为的偏差来实现的。该提议的基本假设是，观察到的运动模式偏差与潜在的生理限制之间存在系统且可量化的关系。当前可用的工具很大程度上无法解析这些关系，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，它们在量化时空复杂运动模式和检测多个节段和关节之间的相互作用方面存在不足。该项目的基本目标是建立一种诊断性多变量技术，用于表征复杂运动模式并将特定运动模式与生理条件相关联。具体来说，拟议的研究将：（i）创建一个“集成多元运动分析”计算工具，将基于形状的分析技术与多元统计工具相结合，以改进复杂运动模式的量化； (ii) 根据特定任务的下肢运动模式库对统计技术进行基准测试，该库是使用数值优化技术生成的，应用于具有无约束和约束关节活动性的下肢简单机械模型； (iii) 确定统计技术能够识别一组受控的、实验性运动捕捉数据中的约束的存在程度和程度，这些数据是在不带或带支架的情况下人为地限制膝盖或脚踝运动的人类行走。我们期望所提议的努力的成功结果将改变步态和其他复杂运动的研究。该项目开发的工具将显着提高诊断能力，有助于评估和治疗运动条件，并允许对各种类群的节段运动进行更准确和全面的比较。 这些工具将对生物和机械系统的复杂性、性能、效率和健康状况产生新颖的推论。该项目还为伊利诺伊大学工程、人类学和心理学领域对运动分析、动力系统计算模拟以及复杂形状的统计比较感兴趣的教师、研究生和本科生提供多学科研究和教育环境和新泽西斯托克顿学院