Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Li, Baoxin
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
Situations of sensory overload are steadily becoming more frequent as the ubiquity of technology approaches reality--particularly with the advent of socio-communicative smartphone applications, and pervasive, high speed wireless networks. Although the ease of accessing information has improved our communication effectiveness and efficiency, our visual and auditory modalities--those modalities that today's computerized devices and displays largely engage--have become overloaded, creating possibilities for distractions, delays and high cognitive load; which in turn can lead to a loss of situational awareness, increasing chances for life threatening situations such as texting while driving. Surprisingly, alternative modalities for information delivery have seen little exploration. Touch, in particular, is a promising candidate given that it is our largest sensory organ with impressive spatial and temporal acuity. Although some approaches have been proposed for touch-based information delivery, they are not without limitations including high learning curves, limited applicability and/or limited expression. This is largely due to the lack of a versatile, comprehensive design theory--specifically, a theory that addresses the design of touch-based building blocks for expandable, efficient, rich and robust touch languages that are easy to learn and use. Moreover, beyond design, there is a lack of implementation and evaluation theories for such languages. To overcome these limitations, a unified, theoretical framework, inspired by natural, spoken language, is proposed called Somatic ABC's for Articulating (designing), Building (developing) and Confirming (evaluating) touch-based languages. To evaluate the usefulness of Somatic ABC's, its design, implementation and evaluation theories were applied to create communication languages for two very unique application areas: audio described movies and motor learning. These applications were chosen as they presented opportunities for complementing communication by offloading information, typically conveyed visually and/or aurally, to the skin. For both studies, it was found that Somatic ABC's aided the design, development and evaluation of rich somatic languages with distinct and natural communication units.
ContributorsMcDaniel, Troy Lee (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2012
Description
Due to the advent of easy-to-use, portable, and cost-effective brain signal sensing devices, pervasive Brain-Machine Interface (BMI) applications using Electroencephalogram (EEG) are growing rapidly. The main objectives of these applications are: 1) pervasive collection of brain data from multiple users, 2) processing the collected data to recognize the corresponding mental states, and 3) providing real-time feedback to the end users, activating an actuator, or information harvesting by enterprises for further services. Developing BMI applications faces several challenges, such as cumbersome setup procedure, low signal-to-noise ratio, insufficient signal samples for analysis, and long processing times. Internet-of-Things (IoT) technologies provide the opportunity to solve these challenges through large scale data collection, fast data transmission, and computational offloading.
This research proposes an IoT-based framework, called BraiNet, that provides a standard design methodology for fulfilling the pervasive BMI applications requirements including: accuracy, timeliness, energy-efficiency, security, and dependability. BraiNet applies Machine Learning (ML) based solutions (e.g. classifiers and predictive models) to: 1) improve the accuracy of mental state detection on-the-go, 2) provide real-time feedback to the users, and 3) save power on mobile platforms. However, BraiNet inherits security breaches of IoT, due to applying off-the-shelf soft/hardware, high accessibility, and massive network size. ML algorithms, as the core technology for mental state recognition, are among the main targets for cyber attackers. Novel ML security solutions are proposed and added to BraiNet, which provide analytical methodologies for tuning the ML hyper-parameters to be secure against attacks.
To implement these solutions, two main optimization problems are solved: 1) maximizing accuracy, while minimizing delays and power consumption, and 2) maximizing the ML security, while keeping its accuracy high. Deep learning algorithms, delay and power models are developed to solve the former problem, while gradient-free optimization techniques, such as Bayesian optimization are applied for the latter. To test the framework, several BMI applications are implemented, such as EEG-based drivers fatigue detector (SafeDrive), EEG-based identification and authentication system (E-BIAS), and interactive movies that adapt to viewers mental states (nMovie). The results from the experiments on the implemented applications show the successful design of pervasive BMI applications based on the BraiNet framework.
This research proposes an IoT-based framework, called BraiNet, that provides a standard design methodology for fulfilling the pervasive BMI applications requirements including: accuracy, timeliness, energy-efficiency, security, and dependability. BraiNet applies Machine Learning (ML) based solutions (e.g. classifiers and predictive models) to: 1) improve the accuracy of mental state detection on-the-go, 2) provide real-time feedback to the users, and 3) save power on mobile platforms. However, BraiNet inherits security breaches of IoT, due to applying off-the-shelf soft/hardware, high accessibility, and massive network size. ML algorithms, as the core technology for mental state recognition, are among the main targets for cyber attackers. Novel ML security solutions are proposed and added to BraiNet, which provide analytical methodologies for tuning the ML hyper-parameters to be secure against attacks.
To implement these solutions, two main optimization problems are solved: 1) maximizing accuracy, while minimizing delays and power consumption, and 2) maximizing the ML security, while keeping its accuracy high. Deep learning algorithms, delay and power models are developed to solve the former problem, while gradient-free optimization techniques, such as Bayesian optimization are applied for the latter. To test the framework, several BMI applications are implemented, such as EEG-based drivers fatigue detector (SafeDrive), EEG-based identification and authentication system (E-BIAS), and interactive movies that adapt to viewers mental states (nMovie). The results from the experiments on the implemented applications show the successful design of pervasive BMI applications based on the BraiNet framework.
ContributorsSadeghi Oskooyee, Seyed Koosha (Author) / Gupta, Sandeep K S (Thesis advisor) / Santello, Marco (Committee member) / Li, Baoxin (Committee member) / Venkatasubramanian, Krishna K (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2020