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Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
Electromyogram (EMG)-based control interfaces are increasingly used in robot teleoperation, prosthetic devices control and also in controlling robotic exoskeletons. Over the last two decades researchers have come up with a plethora of decoding functions to map myoelectric signals to robot motions. However, this requires a lot of training and validation data sets, while the parameters of the decoding function are specific for each subject. In this thesis we propose a new methodology that doesn't require training and is not user-specific. The main idea is to supplement the decoding functional error with the human ability to learn inverse model of an arbitrary mapping function. We have shown that the subjects gradually learned the control strategy and their learning rates improved. We also worked on identifying an optimized control scheme that would be even more effective and easy to learn for the subjects. Optimization was done by taking into account that muscles act in synergies while performing a motion task. The low-dimensional representation of the neural activity was used to control a two-dimensional task. Results showed that in the case of reduced dimensionality mapping, the subjects were able to learn to control the device in a slower pace, however they were able to reach and retain the same level of controllability. To summarize, we were able to build an EMG-based controller for robot devices that would work for any subject, without any training or decoding function, suggesting human-embedded controllers for robotic devices.
ContributorsAntuvan, Chris Wilson (Author) / Artemiadis, Panagiotis (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Created2013
Description
The use of electromyography (EMG) signals to characterize muscle fatigue has been widely accepted. Initial work on characterizing muscle fatigue during isometric contractions demonstrated that its frequency decreases while its amplitude increases with the onset of fatigue. More recent work concentrated on developing techniques to characterize dynamic contractions for use in clinical and training applications. Studies demonstrated that as fatigue progresses, the EMG signal undergoes a shift in frequency, and different physiological mechanisms on the possible cause of the shift were considered. Time-frequency processing, using the Wigner distribution or spectrogram, is one of the techniques used to estimate the instantaneous mean frequency and instantaneous median frequency of the EMG signal using a variety of techniques. However, these time-frequency methods suffer either from cross-term interference when processing signals with multiple components or time-frequency resolution due to the use of windowing. This study proposes the use of the matching pursuit decomposition (MPD) with a Gaussian dictionary to process EMG signals produced during both isometric and dynamic contractions. In particular, the MPD obtains unique time-frequency features that represent the EMG signal time-frequency dependence without suffering from cross-terms or loss in time-frequency resolution. As the MPD does not depend on an analysis window like the spectrogram, it is more robust in applying the timefrequency features to identify the spectral time-variation of the EGM signal.
ContributorsAustin, Hiroko (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Muthuswamy, Jitendran (Committee member) / Arizona State University (Publisher)
Created2012
Description
Myoelectric control is lled with potential to signicantly change human-robot interaction.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
ContributorsIson, Mark (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
Description
All too often, industrial designers face seemingly intractable obstacles as they endeavor to, as Simon (1996, p. 111) describes, devise "courses of action aimed at changing existing situations into preferred ones." These problems, described by Rittel and Webber (1973) as "wicked," are insurmountable due to the contradictory and changing nature of their requirements. I argue that that industrial design (ID) is largely subject to Rittel's quandary because of its penchant for producing single solutions for large populations; such design solutions are bound, in some senses, to fail due to the contradictory and changing nature of large and, thus, inherently diverse populations. This one-size-fits-all approach is not a necessary attribute of ID, rather, it is a consequence of the time in which it came into being, specifically, the period of industrial mass production. Fortunately, new, agile manufacturing techniques, inexpensive sensors, and machine learning provide an alternative course for ID to take, but it requires a new way of thinking and it requires a new set of methods, which I will elaborate in this thesis. According to Duguay, Landry, and Pasin (1997), we are entering an age where it will be feasible to produce individualized, one-off products from large-scale industrial manufacturing facilities in a way that is not only cost effective, but in many ways as cost effective as the existing techniques of mass production. By availing ourselves of these opportunities, we can tame the problem, not by defeating Rittel's logic, rather by reducing the extent to which his theories are appropriate to the domain of ID. This thesis also describes a test study: an experiment whose design was guided by the proposed design methodologies. The goal of the experiment was to determine the feasibility of a noninvasive system for measuring the health of the forearm muscles. Such a tool would provide the basis for assessing the true impact and possible pathogeny of the manual use of products or modifications to products. Previously, it was considered impossible to use surface electromyography (as opposed to needle or wire based electromyography) to assess muscular activity and muscular health due to the complexity of the arrangement of muscles in the forearm. Attempts to overcome this problem have failed because they have tried to create a single solution for all people. My hypothesis is that, by designing for each individual, a solution may be found. Specifically, I show that, for any given individual, there is a high correlation between the EMG signal and the movements of the fingers that, ostensibly, those muscles control. In other words, by knowing, with great accuracy, the position and the motion of the hand then it would become possible to disambiguate the mixed signals coming from the complex web of muscles in the forearm and enable the assessment of the forearm's health by non-invasive means.
ContributorsBraiman, Stuart (Author) / Giard, Jacques (Thesis advisor) / Black Jr., John A (Committee member) / Herring, Donald (Committee member) / Arizona State University (Publisher)
Created2012
Description
How do you convey what’s interesting and important to you as an artist in a digital world of constantly shifting attentions? For many young creatives, the answer is original characters, or OCs. An OC is a character that an artist creates for personal enjoyment, whether based on an already existing story or world, or completely from their own imagination.
As creations made for purely personal interests, OCs are an excellent elevator pitch to talk one creative to another, opening up opportunities for connection in a world where communication is at our fingertips but personal connection is increasingly harder to make. OCs encourage meaningful interaction by offering themselves as muses, avatars, and story pieces, and so much more, where artists can have their characters interact with other creatives through many different avenues such as art-making, table top games, or word of mouth.
In this thesis, I explore the worlds and aesthetics of many creators and their original characters through qualitative research and collaborative art-making. I begin with a short survey of my creative peers, asking general questions about their characters and thoughts on OCs, then move to sketching characters from various creators. I focus my research to a group of seven core creators and their characters, whom I interview and work closely with in order to create a series of seven final paintings of their original characters.
As creations made for purely personal interests, OCs are an excellent elevator pitch to talk one creative to another, opening up opportunities for connection in a world where communication is at our fingertips but personal connection is increasingly harder to make. OCs encourage meaningful interaction by offering themselves as muses, avatars, and story pieces, and so much more, where artists can have their characters interact with other creatives through many different avenues such as art-making, table top games, or word of mouth.
In this thesis, I explore the worlds and aesthetics of many creators and their original characters through qualitative research and collaborative art-making. I begin with a short survey of my creative peers, asking general questions about their characters and thoughts on OCs, then move to sketching characters from various creators. I focus my research to a group of seven core creators and their characters, whom I interview and work closely with in order to create a series of seven final paintings of their original characters.
ContributorsCote, Jacqueline (Author) / Button, Melissa M (Thesis director) / Dove-Viebahn, Aviva (Committee member) / School of Art (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In the past ten years, the United States’ sound recording industries have experienced significant decreases in employment opportunities for aspiring audio engineers from economic imbalances in the music industry’s digital streaming era and reductions in government funding for career and technical education (CTE). The Recording Industry Association of America reports promises of music industry sustainability based on increasing annual revenues in paid streaming services and artists’ high creative demand. The rate of new audio engineer entries in the sound recording subsection of the music industry is not viable to support streaming artists’ high demand to engineer new music recordings. Offering CTE programs in secondary education is rare for aspiring engineers with insufficient accessibility to pursue a post-secondary or vocational education because of financial and academic limitations. These aspiring engineers seek alternatives for receiving an informal education in audio engineering on the Internet using video sharing services like YouTube to search for tutorials and improve their engineering skills. The shortage of accessible educational materials on the Internet restricts engineers from advancing their own audio engineering education, reducing opportunities to enter a desperate job market in need of independent, home studio-based engineers. Content creators on YouTube take advantage of this situation and commercialize their own video tutorial series for free and selling paid subscriptions to exclusive content. This is misleading for newer engineers because these tutorials omit important understandings of fundamental engineering concepts. Instead, content creators teach inflexible engineering methodologies that are mostly beneficial to their own way of thinking. Content creators do not often assess the incompatibility of teaching their own methodologies to potential entrants in a profession that demands critical thinking skills requiring applied fundamental audio engineering concepts and techniques. This project analyzes potential solutions to resolve the deficiencies in online audio engineering education and experiments with structuring simple, deliverable, accessible educational content and materials to new entries in audio engineering. Designing clear, easy to follow material to these new entries in audio engineering is essential for developing a strong understanding for the application of fundamental concepts in future engineers’ careers. Approaches to creating and designing educational content requires translating complex engineering concepts through simplified mediums that reduce limitations in learning for future audio engineers.
ContributorsBurns, Triston Connor (Author) / Tobias, Evan (Thesis director) / Libman, Jeff (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
There has been a recent push for queer fiction, especially in the young adult genre, whose focus is gay and lesbian relationships. This growth is much needed in terms of visibility and the furthering of acceptance, but there are still subjects within the LGBTQ+ community that need to be addressed, including bisexual, asexual, and non-binary erasure. There are many people who claim that these identities do not exist, are labels used as a stepping stone on one's journey to discovering that they are homosexual, or are invented excuses for overtly promiscuous or prudish behavior. The existence of negative stereotypes, particularly those of non-binary individuals, is largely due to a lack of visibility and respectful representation within media and popular culture. However, there is still a dearth of non-binary content in popular literature outside of young adult fiction. Can You See Me? aims to fill the gap in bisexual, asexual, and non-binary representation in adult literature. Each of the four stories that make up this collection deals with an aspect of gender and/or sexuality that has been erased, ignored, or denied visibility in American popular culture. The first story, "We'll Grow Lemon Trees," examines bisexual erasure through the lens of sociolinguistics. A bisexual Romanian woman emigrates to Los Angeles in 1989 and must navigate a new culture, learn new languages, and try to move on from her past life under a dictatorship where speaking up could mean imprisonment or death. The second story "Up, Down, All Around," is about a young genderqueer child and their parents dealing with microaggressions, examining gender norms, and exploring personal identity through imaginary scenarios, each involving an encounter with an unknown entity and a colander. The third story, "Aces High," follows two asexual characters from the day they're born to when they are 28 years old, as they find themselves in pop culture. The two endure identity crises, gender discrimination, erasure, individual obsessions, and prejudice as they learn to accept themselves and embrace who they are. In the fourth and final story, "Mile Marker 72," a gay Mexican man must hide in plain sight as he deals with the death of his partner and coming out to his best friend, whose brother is his partner's murderer.
ContributorsOchser, Jordyn M. (Author) / Bell, Matt (Thesis director) / Free, Melissa (Committee member) / Department of English (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity? Hoping to create a better version of ourselves, The Engineer must navigate the loss of her creation, and Pandora must navigate their new world. The original premiere run was March 27-28, 2018, original cast: Caitlin Andelora, Rikki Tremblay, and Michael Tristano Jr.
ContributorsToye, Abigail Elizabeth (Author) / Linde, Jennifer (Thesis director) / Abele, Kelsey (Committee member) / Department of Information Systems (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
After freelancing on my own for the past year and a half, I have realized that one of the biggest obstacles to college entrepreneurs is a fear or apprehension to sales. As a computer science major trying to sell my services, I discovered very quickly that I had not been prepared for the difficulty of learning sales. Sales get a bad rap and very often is the last thing that young entrepreneurs want to try, but the reality is that sales is oxygen to a company and a required skill for an entrepreneur. Due to this, I compiled all of my knowledge into an e-book for young entrepreneurs starting out to learn how to open up a conversation with a prospect all the way to closing them on the phone. Instead of starting from scratch like I did, college entrepreneurs can learn the bare basics of selling their own services, even if they are terrified of sales and what it entails. In this e-book, there are tips that I have learned to deal with my anxiety about sales such as taking the pressure off of yourself and prioritizing listening more than pitching. Instead of trying to teach sales expecting people to be natural sales people, this e-book takes the approach of helping entrepreneurs that are terrified of sales and show them how they can cope with this fear and still close a client. In the future, I hope young entrepreneurs will have access to more resources that handle this fear and make it much easier for them to learn it by themselves. This e-book is the first step.
ContributorsMead, Kevin Tyler (Author) / Sebold, Brent (Thesis director) / Kruse, Gabriel (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05