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A computational framework for the discovery, modeling, and exploration of task-specific human motor coordination strategies

DiCesare, Christopher A
http://orcid.org/0000-0002-6576-4904
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583998641422141

Abstract Details

2020, PhD, University of Cincinnati, Engineering and Applied Science: Computer Science and Engineering.
Coordinated control of movement is essential for humans. That humans can exhibit complex and diverse motor behavior seemingly effortlessly is an intriguing problem for researchers and practitioners in human movement science. An improved understanding of human motor coordination—its origins, underlying processes, limitations, etc.— holds many implications for how movement-related dysfunction and other problems are addressed. It is essential for individuals who aim to design effective, targeted interventions for improving injury risk, improving rehabilitation outcomes, optimizing function and performance, and informing approaches in other domains, such as intelligent robotics and artificial life. Complicating this understanding, however, is that the processes underlying human motor coordination are complex and not easily observed, analyzed, and interpreted. A significant problem in the study of human motor behavior is understanding how individuals “select” an effective movement solution from among the practically infinite number of possible strategies that will allow for successful task completion. It has been hypothesized that complex movements are produced not by explicit top-down servo-style control, but instead motor system degrees of freedom are organized into higher-order, low-dimensional coordinative structures, or synergies, that simplify movement execution and alleviate the computational burden on the system. In this thesis, a novel computational framework is introduced for the discovery, modeling, and exploration of task-specific human motor coordination strategies using an integrative approach that incorporates biomechanical, systems-based, and neurophysiological perspectives of motor behavior. This framework is developed, tested, and validated by examining task-specific strategies employed in individuals exhibiting complex, jumping and landing behavior. It is used to explore whether strategies are broadly distributed across many distinct, singular strategies, or if task performance is concentrated around a few general coordination strategies. From this, it is ascertained whether strategies for a given task are persistent within or between individuals, and whether there are other unobserved, potentially viable strategies that individuals may adopt. Regarding the latter, an evolutionary algorithmic approach is employed to explore whether alternative, unobserved strategies are possible that lead to successful task performance. This approach utilizes a dynamical exploration of all potentially feasible strategies in a low-dimensional configuration space. The computational approach that is presented in this research is advantageous in the following ways: 1) in using experimentally recorded data from a large group of subjects performing a task, the approach is rooted in biologically grounded, physiologically valid data; and 2) the evolutionary algorithmic approach to exploring human coordination strategies obviates the need for a priori assumptions about how movement is executed that limits scientific and practical intervention. By emphasizing the notion of coordination as an emergent, behavioral phenomenon, this framework exploits the dynamical nature of the human motor system to uncover task-specific motor strategies that emerge under specific individual and task constraints. Ultimately, this approach can provide more pointed insight into and guidance of rehabilitative, assistive, and restorative efforts in human movement science and practice by allowing for the evaluation of motor task performance in practical, applied contexts.
Ali Minai, Ph.D. (Committee Chair)
Raj Bhatnagar, Ph.D. (Committee Member)
Tamara Lorenz, Ph.D. (Committee Member)
Gregory Myer, Ph.D. (Committee Member)
Michael Riley, Ph.D. (Committee Member)
147 p.
Computer Science
biomechanics; coordination; synergies; rehabilitation; optimization

Recommended Citations

Citations

  • DiCesare, C. A. (2020). A computational framework for the discovery, modeling, and exploration of task-specific human motor coordination strategies [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583998641422141

    APA Style (7th edition)

  • DiCesare, Christopher. A computational framework for the discovery, modeling, and exploration of task-specific human motor coordination strategies. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583998641422141.

    MLA Style (8th edition)

  • DiCesare, Christopher. "A computational framework for the discovery, modeling, and exploration of task-specific human motor coordination strategies." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583998641422141

    Chicago Manual of Style (17th edition)

ucin1583998641422141
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This open access ETD is published by University of Cincinnati and OhioLINK.