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

在多片段生物有机体中，复杂运动模式的表征通常是通过识别和测量任务相关行为以及评估偏离这些规范行为来实现的。该建议的基本假设是，在观察到的运动模式偏差和潜在的生理限制之间存在系统和可量化的关系。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，它们在量化时空复杂的运动模式和检测多个部分和关节之间的相互作用方面存在不足。本项目的基本目标是建立一种多变量诊断技术，用于表征复杂的运动模式，并将特定的运动模式与生理条件联系起来。具体而言，拟议的研究将：(i)创建一个“综合多元运动分析”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）将统计技术与特定任务的下肢运动模式库进行基准测试，这些模式库使用数值优化技术生成，应用于具有无约束和约束关节活动的下肢简单力学模型；（iii）确定统计技术能够在一组受控的实验性动作捕捉数据中识别约束的存在和程度的程度，这些数据是关于人类在没有或带有人工约束膝盖或脚踝运动的支架的情况下行走的。我们期望这项工作的成功成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大大提高诊断能力，有助于评估和治疗运动状况，并允许更准确和全面地比较各种分类群的节段运动。这些工具将导致关于生物和机械系统的复杂性，性能，效率和健康的新推论。该项目还为伊利诺伊大学和新泽西州斯托克顿学院的工程、人类学和心理学专业的教师、研究生和本科生提供了一个多学科的研究和教育环境，这些学生对运动分析、动力系统的计算模拟和复杂形状的统计比较感兴趣

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