Browsing by Subject "Motor control"
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Item Comparing deep brain stimulation and levodopa as treatment methods for Parkinson’s disease(2011-05) Robbins, Tiffany Paige; Marquardt, Thomas P.; Sussman, Harvey M.This report will review critically the available research on deep brain stimulation and levodopa as a means of treatment for Parkinson’s disease in an attempt to determine why neither of these treatments improves speech.Item Determining how noise and task redundancy influence motor control of planar reaching(2013-12) Nguyen, Hung Phuc, active 2013; Dingwell, Jonathan B.; Seepersad, Carolyn C.Motor noise and redundancy are vexing issues in motor control; yet their understanding provides great insights on underlying control mechanisms that govern movement. They provide glimpses into how the nervous system organizes and regulates movement within the motor control system. Understand of motor control could spur new advances in motor control could lead to better development of rehabilitation process and technology to counteract debilitating affects of neuromuscular disorders and motor readjustment with prostheses. However, before such process and technology could be developed and adapted for clinical use, a deeper understanding of motor control is needed to unravel the neural roadmap that regulates and generates movement. New theory of motor control could precipitate the development of more robust control mechanisms for robotic-human interaction. This work aims at expanding a more rigorous analytical and mathematical framework to understand how these control mechanisms reconcile redundancy and stochastic noise in human motor control.Item Redundancy reduction in motor control(2015-12) Johnson, Leif Morgan; Ballard, Dana H. (Dana Harry), 1946-; Miikkulainen, Risto; Neptune, Richard; Peters, Jan; Stone, PeterResearch in machine learning and neuroscience has made remarkable progress by investigating statistical redundancy in representations of natural environments, but to date much of this work has focused on sensory information like images and sounds. This dissertation explores the notions of redundancy and efficiency in the motor domain, where several different forms of independence exist. The dissertation begins by discussing redundancy at a conceptual level and presents relevant background material. Next, three main branches of original research are described. The first branch consists of a novel control framework for integrating low-bandwidth sensory updates with model uncertainty and action selection for navigating complex, multi-task environments. The second branch of research applies existing machine learning techniques to movement information and explores the mismatch between these methods for extracting independent components and the forms of redundancy that exist in the motor domain. The third branch of work analyzes full-body, goal-directed reaching movements gathered in a novel laboratory experiment, using explicitly measured information about the goal of each movement to uncover patterns in the movement dynamics. Each branch of research explores redundancy reduction in movement from a different perspective, building up a sort of catalog of the types of information present in movements. Redundancy is discussed throughout as an an important aspect of movement in the natural world. The dissertation concludes by summarizing the contributions of these three branches of work, and discussing promising areas for future work spurred by these investigations. More detailed models of voluntary movements hold promise not only for better treatments, improved prosthetics, smoother animations, and more fluid robots, but also as an avenue for scientific insight into the very foundations of cognition.Item Static and dynamic performance during precision fine motor tracking(2013-05) Gottlich, Samantha; Abraham, Lawrence D.Studies of static and dynamic motor control have a long research history. In most cases, studies have focused on one condition or the other. However, it is important to determine whether differences exist between the two types of task, especially when used in conjunction with task performance. Video game controllers, motorized wheel chairs, steering wheels, and robotic surgical equipment are all examples of how modern equipment uses static and dynamic motor control to achieve task performance goals. To this end, this study aimed to examine possible differences in accuracy or consistency of performance between static and dynamic variations of a precision fine motor tracking task. Nineteen healthy, right-handed volunteer participants were asked to manipulate a cursor to track a moving target with both index fingers, using a static control method in one task and a dynamic control method in another task. The cursor was to follow as closely as possible a target traveling along a diagonal line and back. The control methods were tested during two different testing sessions to reduce confounding of the task conditions. After 50 practice trials in a condition, 5 test trials were recorded. Two dependent variables, RMSE and CVE, were used to represent task performance as indicators of accuracy and consistency, respectively. Analyses of variance with a Latin Square design were used to compare overall performance of each dependent variable between the two conditions. Results showed a significant difference in both variables with p-values less than .001; tracking accuracy was better on the static task and cursor motion consistency was better on the dynamic task. These findings suggest that performance aspects of a fine motor control task does vary with control method and can be used to aid equipment design and task performance in the future.Item A theoretical neuro-biomechanical model of proprioceptive control for lower extremity movement(2012-08) Jin, Hiroshi; Barr, Ronald E.; Arapostathis, Ari, 1954-; Womack, Baxter F.; Neptune, Richard R.; Sreenivasan, S VA computational neural and biomechanical system for human bicycle pedaling is developed in order to study the interaction between the central nervous system and the biomechanical system. It consists of a genetic algorithm, artificial neural network, muscle system, and skeletal system. Our first finding is that the genetic algorithm is a robust tool to formulate human movement. We also find that our developed models are able to handle mechanical perturbation and neural noise. In addition, we observe variability and repeatability of pedaling motion with or without perturbation and noise. Movement phase dependent feedback nature is seen through computation too. This system shows many human movement qualities and is useful for further neural and motor control investigations.Item Trial-to-trial dynamics and learning in generalized, redundant reaching tasks(2010-08) Smallwood, Rachel Fay; Dingwell, Jonathan B.; Abraham, Lawrence D.Trial-to-trial variability in human movement is often overlooked and averaged out, but useful information can be gleaned on the brain’s control of variability. A task can be defined by a function specifying a solution manifold along which all task variable combinations will lead to goal success – the Goal-Equivalent Manifold (GEM). We selected a reaching task with variables reach Distance (D) and reach Time (T). Two GEMs were selected: a constant D/T and constant D×T. Subjects had no knowledge of the goal prior to the experiments and were instructed only to minimize error. Subjects learned the generalized tasks by reducing errors and consolidated learning from one day to the next, generalized learning from the D×T to the D/T GEM, and had interference of learning from the D/T to the D×T GEM. Variability was structured along each GEM significantly more than perpendicular to it. Deviations resulting in errors were corrected significantly more quickly than any other deviation. Our results indicate that subjects can learn generalized reaching tasks, and the brain exploits redundancy in those tasks.