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
Support from the National Science Foundation has led to recent development of techniques to examine body movements in order to improve diagnostic and therapeutic capabilities. These new techniques use data collected by computerized motion capture technology to better quantify changes in movement symmetry, timing, and coupling. These techniques use dynamics, biomechanics, signal processing, shape analysis, and multivariate statistics to examine the complex interaction between joints and body segments used while we move. With the advent of motion capture technology, it is possible to record body movements during specific tasks such as walking, running, etc. However, most current methods for analyzing these data primarily examine discrete events, focus primarily on single joints, or use relatively simple measures of motion (e.g., stride and step length). These measures are unable to capture the dynamic aspects of the motion, such as simultaneous movements, asymmetries across multiple joints that occur at different times throughout a complete stride (or gait cycle), or the coupling effect of non-local responses to perturbations (such as an injury to one joint). Thus, these approaches fail to exploit the richness of the motion capture data for providing better understanding of complex motion patterns. Walking is a spatiotemporally complex behavior. Each body segment is connected to another segment, and the motion of each body part is thus coupled in space and time to that of other body parts during walking. Changes to the motion of any joint, for example due to physiological limitations, should cause correlated responses in other joints within the same (ipsilateral) and contralateral limbs. This effect is often seen in gait compensation strategies that are caused by trauma or pathology, such as leg injuries or stroke. Physical development across the lifespan also result in changes in movement strategies, from crawling to initial walking to mature walking to aged walking. Yet, few analysis tools have been developed that can quantitatively characterize these changes or differences in movement patterns. Combining skills in dynamics, biomechanics, signal processing, shape analysis, and multivariate statistics, this multidisciplinary team from the University of Illinois at Urbana-Champaign, consisting of mechanical engineers Elizabeth Hsiao-Wecksler and Harry Dankowicz, physical anthropologist John Polk, and quantitative psychologist Sungjin Hong, has worked with physical anthropologist Michael Lague at the Richard Stockton College of New Jersey and developmental psychologist Karl Rosengren at Northwestern University. Along with graduate and undergraduate students from these disciplines, this group has identified new analysis techniques based on motion capture data. Different techniques have been developed to characterize periods of deviation from joint symmetry; quantify the complexity and variability of phase portrait shapes; explore multivariate alignment and decomposition of gait shapes derived from joint center locations; identify temporal cross-correlations of joint movements; and improve time-normalization methods. These techniques can be used to characterize the complex and coupled nature of human gait in patient populations to not only diagnose or track pathology recovery, but to provide insight into control mechanisms that underlie gait pathology, and to characterize the patterns of interrelationships among movement patterns. The results of the overall research program have been presented at over 20 national and international scientific meetings and documented in at least seven scholarly articles published in biomedical journals such as Journal of Biomechanics, Gait & Posture and Clinical Biomechanics. It is expected that the techniques developed in this effort will significantly advance diagnostic capabilities, aid in the evaluation and treatment of movement conditions, and permit more accurate and comprehensive comparisons of segmental movements due to development or evolution. Members of the research team are already collaborating with national and international researchers and clinicians to apply these new techniques to better understand changes in movement due to (a) neuro-motor control impairment associated with stroke, Parkinson’s disease, aging, autism, and developmental disorders; (b) strengthening therapy of lower-limb amputees; (c) propulsion mechanics and shoulder pain in manual wheelchair users; and (d) even knee pathology in dogs.
