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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


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 …

Contributors
Chattopadhyay, Rita, Panchanathan, Sethuraman, Ye, Jieping, et al.
Created Date
2013

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 …

Contributors
Ison, Mark, Artemiadis, Panagiotis, Santello, Marco, et al.
Created Date
2015