Variability, Symmetry, and Dynamics in Human Rhythmic Motor Control
Ankarali, Mustafa M.
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How humans and other animals control rhythmic behaviors, and locomotion in particular, is one of the grand challenges of neuroscience and biomechanics. And yet remarkably few studies address the fundamental control-systems modeling of locomotor control. This thesis attempts to address several pieces of this grand challenge through the development of experimental, theoretical, and computational tools. Specifically, we focus our attention on three key features of human rhythmic motor control, namely variability, symmetry, and dynamics. Variability: Little is known about how haptic sensing of discrete events, such as heel-strike in walking, in rhythmic dynamic tasks enhances behavior and performance. In order to discover the role of discrete haptic cues on rhythmic motor control performance, we study a virtual paddle juggling behavior. We show that haptic sensing of a force impulse to the hand at the moment of ball-paddle collision categorically improves performance over visual feedback alone, not by regulating the rate of convergence to steady state, but rather by reducing cycle-to-cycle variability. Symmetry: Neglecting evident characteristics of a system can certainly be a modeling convenience, but it may also produce a better statistical model. For example, the dynamics of human locomotion is frequently treated as symmetric about the sagittal plane for modeling convenience. In this work, we test this assumption by examining the statistical consequences of neglecting (or not) bilateral asymmetries in the dynamics of human walking. Indeed, we show that there are statistically significant asymmetries in the walking dynamics of healthy participants (N=8), but that by ignoring these asymmetries and fitting a symmetric model to the data, we arrive at a more consistent and predictive model of human walking. Dynamics: Rhythmic hybrid dynamic behaviors can be observed in a wide variety of biological and robotic systems. Analytic (white-box) modeling tools of such systems are limited to the case when we have a full (and preferably simple) mathematical model that can accurately describe the system dynamics. In contrast, data-driven (block-box) system identification methods have the potential to overcome this fundamental limitation and could play a critical role in describing and analyzing the dynamics of rhythmic behaviors based on experimental data. And yet few tools exist for identifying the dynamics of rhythmic systems from input--output data. In this context, we propose a new formulation for identifying the dynamics of rhythmic hybrid dynamical systems around their limit-cycles by using discrete-time harmonic transfer functions.