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- Genre: Academic theses
- Creators: Norton, Kay
- Creators: Turaga, Pavan
Description
In this thesis, quantitative evaluation of quality of movement during stroke rehabilitation will be discussed. Previous research on stroke rehabilitation in hospital has been shown to be effective. In this thesis, we study various issues that arise when creating a home-based system that can be deployed in a patient's home. Limitation of motion capture due to reduced number of sensors leads to problems with design of kinematic features for quantitative evaluation. Also, the hierarchical three-level tasks of rehabilitation requires new design of kinematic features. In this thesis, the design of kinematic features for a home based stroke rehabilitation system will be presented. Results of the most challenging classifier are shown and proves the effectiveness of the design. Comparison between modern classification techniques and low computational cost threshold based classification with same features will also be shown.
ContributorsCheng, Long (Author) / Turaga, Pavan (Thesis advisor) / Arizona State University (Publisher)
Created2012
Description
Arnold Schoenberg's 1908-09 song cycle, Das Buch der hängenden Gärten [The Book of the Hanging Gardens], opus 15, represents one of his most decisive early steps into the realm of musical modernism. In the midst of personal and artistic crises, Schoenberg set texts by Stefan George in a style he called "pantonality," and described his composition as radically new. Though stylistically progressive, however, Schoenberg's musical achievement had certain ideologically conservative roots: the composer numbered among turn-of-the-century Viennese artists and thinkers whose opposition to the conventional and the popular--in favor of artistic autonomy and creativity--concealed a reactionary misogyny. A critical reading of Hanging Gardens through the lens of gender reveals that Schoenberg, like many of his contemporaries, incorporated strong frauenfeindlich [anti-women] elements into his work, through his modernist account of artistic creativity, his choice of texts, and his musical settings. Although elements of Hanging Gardens' atonal music suggest that Schoenberg valued gendered-feminine principles in his compositional style, a closer analysis of the work's musical language shows an intact masculinist hegemony. Through his deployment of uncanny tonal reminiscences, underlying tonal gestures, and closed forms in Hanging Gardens, Schoenberg ensures that the feminine-associated "excesses" of atonality remain under masculine control. This study draws upon the critical musicology of Susan McClary while arguing that Schoenberg's music is socially contingent, affected by the gender biases of his social and literary milieux. It addresses likely influences on Schoenberg's worldview including the philosophy of Otto Weininger, Freudian psychoanalysis, and a complex web of personal relationships. Finally, this analysis highlights the relevance of Schoenberg's world and its constructions of gender to modern performance practice, and argues that performers must consider interrelated historical, textual, and musical factors when interpreting Hanging Gardens in new contexts.
ContributorsGinger, Kerry Anne (Author) / FitzPatrick, Carole (Thesis advisor) / Dreyfoos, Dale (Committee member) / Mook, Richard (Committee member) / Norton, Kay (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2012
Description
Motion capture using cost-effective sensing technology is challenging and the huge success of Microsoft Kinect has been attracting researchers to uncover the potential of using this technology into computer vision applications. In this thesis, an upper-body motion analysis in a home-based system for stroke rehabilitation using novel RGB-D camera - Kinect is presented. We address this problem by first conducting a systematic analysis of the usability of Kinect for motion analysis in stroke rehabilitation. Then a hybrid upper body tracking approach is proposed which combines off-the-shelf skeleton tracking with a novel depth-fused mean shift tracking method. We proposed several kinematic features reliably extracted from the proposed inexpensive and portable motion capture system and classifiers that correlate torso movement to clinical measures of unimpaired and impaired. Experiment results show that the proposed sensing and analysis works reliably on measuring torso movement quality and is promising for end-point tracking. The system is currently being deployed for large-scale evaluations.
ContributorsDu, Tingfang (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Rikakis, Thanassis (Committee member) / Arizona State University (Publisher)
Created2012
Description
ABSTRACT This document introduces singers and voice teachers to Dr. Alfred A. Tomatis's listening training method with a particular emphasis on its relevance to singers. After presenting an overview of Tomatis's work in the field of audio-psycho-phonology (circa 1947 through the 1990s) and specific ways that aspects of his theory are relevant to singers' performance skills, this project investigates the impact of listening training on singers by examining published research. The studies described in this document have investigated the impact of listening training on elements of the singer's skill set, including but not limited to measures of vocal quality such as intonation, vocal control, intensity, and sonority, as well as language pronunciation and general musicianship. Anecdotal evidence, presented by performers and their observers, is also considered. The evidence generated by research studies and anecdotal reports strongly favors Tomatis-based listening training as a valid way to improve singers' performance abilities.
ContributorsHurley, Susan Lynn (Author) / Doan, Jerry (Thesis advisor) / Dreyfoos, Dale (Committee member) / Kopta, Anne (Committee member) / Norton, Kay (Committee member) / Thompson, Billie M (Committee member) / Arizona State University (Publisher)
Created2012
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
William Levi Dawson (1899-1990), director of the Tuskegee Institute Choir from 1931 to 1956, was one of the most important arrangers of Negro spirituals in the twentieth century. He is also remembered as an outstanding composer, conductor, speaker, and leader of festival choruses. His arrangements are still sung by choirs all over the world. Save a small number of dissertations and various articles, however, very little has been written about him. In fact, almost no significant writing has been undertaken utilizing the Dawson papers held at the Manuscript, Archives, and Rare Books Library at Emory University in Atlanta, Georgia. This study utilizes that collection in examining four areas of Dawson's life: his work as a composer, his work as an arranger of Negro spirituals, his work as a choral conductor and music pedagogue, and his life as an African American man living in segregated times. Dawson is shown as a thoughtful, deliberate practitioner of his art who built his career with intention, and who, through his various activities, sought both to affirm the traditional music of his people and to transcend his era's problems with the definitions, associations, and prejudices attached to the term "race." Using a diverse selection of letters, notes, and speeches held in the archive, it is possible to develop a fuller, more nuanced portrait of Dawson. Through a thorough examination of a select few of these documents, his growth can be traced from a young composer living in Chicago, to a college choral director dealing with the realities of racial inequality in the mid-twentieth century, to a seasoned, respected elder in his field, endeavoring to pass on to others knowledge of the music he spent his life arranging and teaching.
ContributorsHuff, Vernon Edward (Author) / Schildkret, David (Thesis advisor) / Norton, Kay (Committee member) / Tobias, Evan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Throughout history composers and artists have been inspired by the natural world. Nature's influence on music is extraordinary, though water in particular, has had a unique magnetic pull. The large number of compositions dealing with water, from Handel's Water Music (1717) to Ros Bandt's and Leah Barclay's Rivers Talk (2012), reflects this continuous fascination. Since the late 1940s, composers have ventured further and brought actual sounds from the environment, including water recorded on tape, into the musical arena. Moreover, since the 1960s, some composers have nudged their listeners to become more ecologically aware. Much skepticism exists, as with any unconventional idea in history, and as a result compositions belonging to this realm of musique concrète are not as widely recognized and examined as they should be. In this thesis, I consider works of three composers: Annea Lockwood, Eve Beglarian, and Leah Barclay, who not only draw inspiration from nature, but also use their creativity to call attention to pristine environments. All three composers embrace the idea that music can be broadly defined and use technology as a tool to communicate their artistic visions. These artists are from three different countries and represent three generations of composers who set precedents for a new way of composing, listening to, performing, and thinking about music and the environment. This thesis presents case studies of Lockwood's A Sound Map of the Danube River, Beglarian's Mississippi River Project, and Barclay's Sound Mirrors. This thesis draws on unpublished correspondence with the composers, analytical theories of R. Murray Schafer, Barry Truax, and Martijn Voorvelt, among others, musicological publications, eco-critical and environmental studies by Al Gore, Bill McKibben, and Vandana Shiva, as well as research by feminist scholars. As there is little written on music and nature from an eco-critical and eco-feminist standpoint, this thesis will contribute to the recognition of significant figures in contemporary music that might otherwise be overlooked. In this study I maintain that composers and sound artists engage with sounds in ways that reveal aspects of particular places, and their attitudes toward these places to lead listeners toward a greater ecological awareness.
ContributorsRichardson, Jamilyn (Author) / Feisst, Sabine (Thesis advisor) / Solís, Ted (Committee member) / Norton, Kay (Committee member) / Arizona State University (Publisher)
Created2012
Description
Effective modeling of high dimensional data is crucial in information processing and machine learning. Classical subspace methods have been very effective in such applications. However, over the past few decades, there has been considerable research towards the development of new modeling paradigms that go beyond subspace methods. This dissertation focuses on the study of sparse models and their interplay with modern machine learning techniques such as manifold, ensemble and graph-based methods, along with their applications in image analysis and recovery. By considering graph relations between data samples while learning sparse models, graph-embedded codes can be obtained for use in unsupervised, supervised and semi-supervised problems. Using experiments on standard datasets, it is demonstrated that the codes obtained from the proposed methods outperform several baseline algorithms. In order to facilitate sparse learning with large scale data, the paradigm of ensemble sparse coding is proposed, and different strategies for constructing weak base models are developed. Experiments with image recovery and clustering demonstrate that these ensemble models perform better when compared to conventional sparse coding frameworks. When examples from the data manifold are available, manifold constraints can be incorporated with sparse models and two approaches are proposed to combine sparse coding with manifold projection. The improved performance of the proposed techniques in comparison to sparse coding approaches is demonstrated using several image recovery experiments. In addition to these approaches, it might be required in some applications to combine multiple sparse models with different regularizations. In particular, combining an unconstrained sparse model with non-negative sparse coding is important in image analysis, and it poses several algorithmic and theoretical challenges. A convex and an efficient greedy algorithm for recovering combined representations are proposed. Theoretical guarantees on sparsity thresholds for exact recovery using these algorithms are derived and recovery performance is also demonstrated using simulations on synthetic data. Finally, the problem of non-linear compressive sensing, where the measurement process is carried out in feature space obtained using non-linear transformations, is considered. An optimized non-linear measurement system is proposed, and improvements in recovery performance are demonstrated in comparison to using random measurements as well as optimized linear measurements.
ContributorsNatesan Ramamurthy, Karthikeyan (Author) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Karam, Lina (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
With robots being used extensively in various areas, a certain degree of robot autonomy has always been found desirable. In applications like planetary exploration, autonomous path planning and navigation are considered essential. But every now and then, a need to modify the robot's operation arises, a need for a human to provide it some supervisory parameters that modify the degree of autonomy or allocate extra tasks to the robot. In this regard, this thesis presents an approach to include a provision to accept and incorporate such human inputs and modify the navigation functions of the robot accordingly. Concepts such as applying kinematical constraints while planning paths, traversing of unknown areas with an intent of maximizing field of view, performing complex tasks on command etc. have been examined and implemented. The approaches have been tested in Robot Operating System (ROS), using robots such as the iRobot Create, Personal Robotics (PR2) etc. Simulations and experimental demonstrations have proved that this approach is feasible for solving some of the existing problems and that it certainly can pave way to further research for enhancing functionality.
ContributorsVemprala, Sai Hemachandra (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
As an organist, church musician, and educator, Clifford Demarest (1874-1946) was a prominent figure in New York during the first half of the twentieth century. However, prior to this thesis, Demarest's place within the history of American music, like that of many of his contemporaries, was all but neglected. This research reveals Clifford Demarest as an influential figure in American musical history from around 1900 to his retirement in 1937. Led by contemporary accounts, I trace Demarest's musical influence through his three musical careers: professional organist, church musician, and educator. As a prominent figure in the fledgling American Guild of Organists, Demarest was dedicated to the unification of its members and the artistic legitimacy of the organist profession. As the organist and choir director of the Church of the Messiah, later the Community Church of New York (1911-1946, inclusive), Demarest played an integral part in the liberal atmosphere fostered by the congregation's minister, John Haynes Holmes (1879-1964). Together Holmes and Demarest directly influenced the nascent National Association for the Advancement of Colored People and supported luminaries of the Harlem Renaissance. Influential figures such as Langston Hughes (1902-1967), Augustus Granville Dill (1881-1956), Egbert Ethelred Brown (1875-1956), and Countee Cullen (1903-1946) were inspired by the liberal environment in the Church of the Messiah; however, prior to this research, their connections to the church were unexplored. As the music supervisor of Tenafly High School and later, for the state of New Jersey, Demarest influenced countless students through his passion for music. His compositions for student orchestras are among the earliest to elevate the artistic standards of school music ensembles during the first four decades of the twentieth century. Archival sources such as church records, letters, and newspaper editorials, are synthesized with current research to characterize Demarest's place in these three professional orbits of the early twentieth century. His story also represents those of countless other working musicians from his era that have been forgotten. Therefore, this research opens an important new research field – a window into the dynamic world of the American organist.
ContributorsHicks, Glen W (Author) / Saucier, Catherine (Thesis advisor) / Norton, Kay (Thesis advisor) / Holbrook, Amy (Committee member) / Arizona State University (Publisher)
Created2014