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- All Subjects: Parkinson's disease
Description
Parkinson's disease is a neurodegenerative disorder in the central nervous system that affects a host of daily activities and involves a variety of symptoms; these include tremors, slurred speech, and rigid muscles. It is the second most common movement disorder globally. In Stage 3 of Parkinson's, afflicted individuals begin to develop an abnormal gait pattern known as freezing of gait (FoG), which is characterized by decreased step length, shuffling, and eventually complete loss of movement; they are unable to move, and often results in a fall. Surface electromyography (sEMG) is a diagnostic tool to measure electrical activity in the muscles to assess overall muscle function. Most conventional EMG systems, however, are bulky, tethered to a single location, expensive, and primarily used in a lab or clinical setting. This project explores an affordable, open-source, and portable platform called Open Brain-Computer Interface (OpenBCI). The purpose of the proposed device is to detect gait patterns by leveraging the surface electromyography (EMG) signals from the OpenBCI and to help a patient overcome an episode using haptic feedback mechanisms. Previously designed devices with similar intended purposes utilize accelerometry as a method of detection as well as audio and visual feedback mechanisms in their design.
ContributorsAnantuni, Lekha (Author) / McDaniel, Troy (Thesis director) / Tadayon, Arash (Committee member) / Harrington Bioengineering Program (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The artificial neural network is a form of machine learning that is highly effective at recognizing patterns in large, noise-filled datasets. Possessing these attributes uniquely qualifies the neural network as a mathematical basis for adaptability in personal biomedical devices. The purpose of this study was to determine the viability of neural networks in predicting Freezing of Gait (FoG), a symptom of Parkinson's disease in which the patient's legs are suddenly rendered unable to move. More specifically, a class of neural networks known as layered recurrent networks (LRNs) was applied to an open- source FoG experimental dataset donated to the Machine Learning Repository of the University of California at Irvine. The independent variables in this experiment \u2014 the subject being tested, neural network architecture, and sampling of the majority classes \u2014 were each varied and compared against the performance of the neural network in predicting future FoG events. It was determined that single-layered recurrent networks are a viable method of predicting FoG events given the volume of the training data available, though results varied significantly between different patients. For the three patients tested, shank acceleration data was used to train networks with peak precision/recall values of 41.88%/47.12%, 89.05%/29.60%, and 57.19%/27.39% respectively. These values were obtained for networks optimized using detection theory rather than optimized for desired values of precision and recall. Furthermore, due to the nature of the experiments performed in this study, these values are representative of the lower-bound performance of layered recurrent networks trained to detect gait freezing. As such, these values may be improved through a variety of measures.
ContributorsZia, Jonathan Sargon (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Adler, Charles (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Although many data visualization diagrams can be made accessible for individuals who are blind or visually impaired, they often do not present the information in a way that intuitively allows readers to easily discern patterns in the data. In particular, accessible node graphs tend to use speech to describe the transitions between nodes. While the speech is easy to understand, readers can be overwhelmed by too much speech and may not be able to discern any structural patterns which occur in the graphs. Considering these limitations, this research seeks to find ways to better present transitions in node graphs.
This study aims to gain knowledge on how sequence patterns in node graphs can be perceived through speech and nonspeech audio. Users listened to short audio clips describing a sequence of transitions occurring in a node graph. User study results were evaluated based on accuracy and user feedback. Five common techniques were identified through the study, and the results will be used to help design a node graph tool to improve accessibility of node graph creation and exploration for individuals that are blind or visually impaired.
This study aims to gain knowledge on how sequence patterns in node graphs can be perceived through speech and nonspeech audio. Users listened to short audio clips describing a sequence of transitions occurring in a node graph. User study results were evaluated based on accuracy and user feedback. Five common techniques were identified through the study, and the results will be used to help design a node graph tool to improve accessibility of node graph creation and exploration for individuals that are blind or visually impaired.
ContributorsDarmawaskita, Nicole (Author) / McDaniel, Troy (Thesis director) / Duarte, Bryan (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
In this project, I investigated the impact of virtual reality on memory retention. The investigative approach to see the impact of virtual reality on memory retention, I utilized the memorization technique called the memory palace in a virtual reality environment. For the experiment, due to Covid-19, I was forced to be the only subject. To get effective data, I tested myself within randomly generated environments with a completely unique set of objects, both outside of a virtual reality environment and within one. First I conducted a set of 10 tests on myself by going through a virtual environment on my laptop and recalling as many objects I could within that environment. I recorded the accuracy of my own recollection as well as how long it took me to get through the data. Next I conducted a set of 10 tests on myself by going through the same virtual environment, but this time with an immersive virtual reality(VR) headset and a completely new set of objects. At the start of the project it was hypothesized that virtual reality would result in a higher memory retention rate versus simply going through the environment in a non-immersive environment. In the end, the results, albeit with a low test rate, leaned more toward showing the hypothesis to be true rather than not.
ContributorsDu, Michael Shan (Author) / Kobayashi, Yoshihiro (Thesis director) / McDaniel, Troy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05