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- Creators: Berger, Christopher
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
This research study examined the bilateral asymmetry found in muscle pairs including the right and left sides of the upper rectus abdominis, lower rectus abdominis, external oblique, and internal oblique in college-aged, apparently fit men and women. Bilateral symmetry was found using surface electromyography (EMG) during three core exercises: 1) ab-slides using paper plates (paper), 2) planks, and 3) ab-slides using a commercial AbSlide® roller device by comparing maximal voluntary contractions (MVCs) of the four muscles previously listed. This research analyzed the percentage of muscle activation during these exercises to each person’s MVC using Noraxon® software. Analysis found that asymmetry for each muscle group was present although there is no measure of clinical significance for symmetry scores of the core muscles yet.
Asymmetry scores were calculated for all three exercises. The exercise that produced the greatest absolute, average asymmetry score was the ab-slide using the roller device. The muscle that the greatest absolute asymmetry was found was the internal oblique. This means that during the three exercises and MVC, the greatest difference between right and left side pair muscles was observed in the internal obliques. The standard deviation of symmetry scores for all exercises and muscles was great as there was much variation in the skill levels in the participants of this study. Bilateral asymmetry was found by visually comparing the asymmetry scores. In conclusion, bilateral asymmetry was found in the core muscles of college-aged individuals during bilateral abdominal exercises.
Asymmetry scores were calculated for all three exercises. The exercise that produced the greatest absolute, average asymmetry score was the ab-slide using the roller device. The muscle that the greatest absolute asymmetry was found was the internal oblique. This means that during the three exercises and MVC, the greatest difference between right and left side pair muscles was observed in the internal obliques. The standard deviation of symmetry scores for all exercises and muscles was great as there was much variation in the skill levels in the participants of this study. Bilateral asymmetry was found by visually comparing the asymmetry scores. In conclusion, bilateral asymmetry was found in the core muscles of college-aged individuals during bilateral abdominal exercises.
ContributorsFavaro, Miguel Angel (Author) / Berger, Christopher (Thesis director) / Lorenz, Kent (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2015-05