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Building adaptive computational systems for physiological and biomedical data

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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.…

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
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Upper-Extremity Exoskeleton

Description

In nature, some animals have an exoskeleton that provides protection, strength, and stability to the organism, but in engineering, an exoskeleton refers to a device that augments or aids human ability. Since the 1890s, engineers have been designing exoskeletal devices, and conducting research into the possible uses of such devices.…

In nature, some animals have an exoskeleton that provides protection, strength, and stability to the organism, but in engineering, an exoskeleton refers to a device that augments or aids human ability. Since the 1890s, engineers have been designing exoskeletal devices, and conducting research into the possible uses of such devices. These bio-inspired mechanisms do not necessarily relate to a robotic device, though since the 1900s, robotic principles have been applied to the design of exoskeletons making their development a subfield in robotic research. There are different multiple types of exoskeletons that target different areas of the human body, and the targeted area depends on the need of the device. Usually, the devices are developed for medical or military usage; for this project, the focus is on medical development of an automated elbow joint to assist in rehabilitation. This project is being developed for therapeutic purposes in conjunction between Arizona State University and Mayo Clinic. Because of the nature of this project, I am responsible for the development of a lightweight brace that could be applied to the elbow joint that was designed by Dr. Kevin Hollander. In this project, my research centered on the use of the Wilmer orthosis brace design, and its possible application to the exoskeleton elbow being developed for Mayo Clinic. This brace is a lightweight solution that provides extra comfort to the user.
ContributorsCarlton, Bryan (Author) / Sugar, Thomas (Thesis director) / Aukes, Daniel (Committee member) / Barrett, The Honors College (Contributor) / Engineering Programs (Contributor)
Created2022-05