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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Contributor
Date Range
2011 2019


The ultimate goal of human movement control research is to understand how natural movements performed in daily activities, are controlled. Natural movements require coordination of multiple degrees of freedom (DOF) of the arm. Here, patterns of arm joint control during daily functional tasks were examined, which are performed through rotation of the shoulder, elbow, and wrist with the use of seven DOF: shoulder flexion/extension, abduction/adduction, and internal/external rotation; elbow flexion/extension and pronation/supination; wrist flexion/extension and radial/ulnar deviation. Analyzed movements imitated two activities of daily living: combing the hair and turning the page in a book. Kinematic and kinetic analyses were …

Contributors
Marshall, Dirk, Dounskaia, Natalia, Schaefer, Sydney, et al.
Created Date
2018

Understanding human-human interactions during the performance of joint motor tasks is critical for developing rehabilitation robots that could aid therapists in providing effective treatments for motor problems. However, there is a lack of understanding of strategies (cooperative or competitive) adopted by humans when interacting with other individuals. Previous studies have investigated the cues (auditory, visual and haptic) that support these interactions but understanding how these unconscious interactions happen even without those cues is yet to be explained. To address this issue, in this study, a paradigm that tests the parallel efforts of pairs of individuals (dyads) to complete a jointly …

Contributors
Agrawal, Ankit, Buneo, Christopher, Santello, Marco, et al.
Created Date
2016

Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic arm movement). Operation of these devices was not automatic, and subjects needed calibration and training in order to master this control. In short, learning became a key component in controlling these systems. As a result, BMIs have become ideal tools to probe and explore brain activity, since they allow the isolation of neural inputs and systematic altering of the relationships between the neural signals and output. I have used BMIs …

Contributors
Armenta Salas, Michelle, Helms Tillery, Stephen I, Si, Jennie, et al.
Created Date
2015

Learning by trial-and-error requires retrospective information that whether a past action resulted in a rewarded outcome. Previous outcome in turn may provide information to guide future behavioral adjustment. But the specific contribution of this information to learning a task and the neural representations during the trial-and-error learning process is not well understood. In this dissertation, such learning is analyzed by means of single unit neural recordings in the rats' motor agranular medial (AGm) and agranular lateral (AGl) while the rats learned to perform a directional choice task. Multichannel chronic recordings using implanted microelectrodes in the rat's brain were essential to …

Contributors
Yuan, Yuan, Si, Jennie, Buneo, Christopher, et al.
Created Date
2014

Growing understanding of the neural code and how to speak it has allowed for notable advancements in neural prosthetics. With commercially-available implantable systems with bi- directional neural communication on the horizon, there is an increasing imperative to develop high resolution interfaces that can survive the environment and be well tolerated by the nervous system under chronic use. The sensory encoding aspect optimally interfaces at a scale sufficient to evoke perception but focal in nature to maximize resolution and evoke more complex and nuanced sensations. Microelectrode arrays can maintain high spatial density, operating on the scale of cortical columns, and can …

Contributors
Oswalt, Denise, Greger, Bradley, Buneo, Christopher, et al.
Created Date
2018

Existing theories suggest that evidence is accumulated before making a decision with competing goals. In motor tasks, reward and motor costs have been shown to influence the decision, but the interaction between these two variables has not been studied in depth. A novel reward-based sensorimotor decision-making task was developed to investigate how reward and motor costs interact to influence decisions. In human subjects, two targets of varying size and reward were presented. After a series of three tones, subjects initiated a movement as one of the targets disappeared. Reward was awarded when participants reached through the remaining target within a …

Contributors
Boege, Scott, Santello, Marco, Fine, Justin, et al.
Created Date
2019

Tracking microscale targets in soft tissue using implantable probes is important in clinical applications such as neurosurgery, chemotherapy and in neurophysiological application such as brain monitoring. In most of these applications, such tracking is done with visual feedback involving some imaging modality that helps localization of the targets through images that are co-registered with stereotaxic coordinates. However, there are applications in brain monitoring where precision targeting of microscale targets such as single neurons need to be done in the absence of such visual feedback. In all of the above mentioned applications, it is important to understand the dynamics of mechanical …

Contributors
Talebianmoghaddam, Shahrzad, Muthuswamy, Jitendran, Towe, Bruce, et al.
Created Date
2015

Lower-limb prosthesis users have commonly-recognized deficits in gait and posture control. However, existing methods in balance and mobility analysis fail to provide sufficient sensitivity to detect changes in prosthesis users' postural control and mobility in response to clinical intervention or experimental manipulations and often fail to detect differences between prosthesis users and non-amputee control subjects. This lack of sensitivity limits the ability of clinicians to make informed clinical decisions and presents challenges with insurance reimbursement for comprehensive clinical care and advanced prosthetic devices. These issues have directly impacted clinical care by restricting device options, increasing financial burden on clinics, and …

Contributors
Howard, Charla Lindley, Abbas, James, Buneo, Christopher, et al.
Created Date
2017

This dissertation includes two parts. First it focuses on discussing robust signal processing algorithms, which lead to consistent performance under perturbation or uncertainty in video target tracking applications. Projective distortion plagues the quality of long sequence mosaicking which results in loosing important target information. Some correction techniques require prior information. A new algorithm is proposed in this dissertation to this very issue. Optimization and parameter tuning of a robust camera motion estimation as well as implementation details are discussed for a real-time application using an ordinary general-purpose computer. Performance evaluations on real-world unmanned air vehicle (UAV) videos demonstrate the robustness …

Contributors
Yang, Chenhui, Si, Jennie, Jassemidis, Leonidas, et al.
Created Date
2012

Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following …

Contributors
Anand, Sindhu, Muthuswamy, Jitendran, Tillery, Stephen H, et al.
Created Date
2013