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

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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