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Multisource domain adaptation and its application to early detection of fatigue

Multisource domain adaptation and its application to early detection of fatigue Multisource Domain Adaptation and Its Application to Early Detection of Fatigue RITA CHATTOPADHYAY and QIAN SUN, Arizona State University WEI FAN, IBM T.J. Watson Research IAN DAVIDSON, University of California, Davis SETHURAMAN PANCHANATHAN and JIEPING YE, Arizona State University We consider the characterization of muscle fatigue through a noninvasive sensing mechanism such as Surface ElectroMyoGraphy (SEMG). While changes in the properties of SEMG signals with respect to muscle fatigue have been reported in the literature, the large variation in these signals across different individuals makes the task of modeling and classification of SEMG signals challenging. Indeed, the variation in SEMG parameters from subject to subject creates differences in the data distribution. In this article, we propose two transfer learning frameworks based on the multisource domain adaptation methodology for detecting different stages of fatigue using SEMG signals, that addresses the distribution differences. In the proposed frameworks, the SEMG data of a subject represent a domain; data from multiple subjects in the training set form the multiple source domains and the test subject data form the target domain. SEMG signals are predominantly different in conditional probability distribution across subjects. The key feature of the first framework is a novel weighting http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Multisource domain adaptation and its application to early detection of fatigue

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/2382577.2382582
Publisher site
See Article on Publisher Site

Abstract

Multisource Domain Adaptation and Its Application to Early Detection of Fatigue RITA CHATTOPADHYAY and QIAN SUN, Arizona State University WEI FAN, IBM T.J. Watson Research IAN DAVIDSON, University of California, Davis SETHURAMAN PANCHANATHAN and JIEPING YE, Arizona State University We consider the characterization of muscle fatigue through a noninvasive sensing mechanism such as Surface ElectroMyoGraphy (SEMG). While changes in the properties of SEMG signals with respect to muscle fatigue have been reported in the literature, the large variation in these signals across different individuals makes the task of modeling and classification of SEMG signals challenging. Indeed, the variation in SEMG parameters from subject to subject creates differences in the data distribution. In this article, we propose two transfer learning frameworks based on the multisource domain adaptation methodology for detecting different stages of fatigue using SEMG signals, that addresses the distribution differences. In the proposed frameworks, the SEMG data of a subject represent a domain; data from multiple subjects in the training set form the multiple source domains and the test subject data form the target domain. SEMG signals are predominantly different in conditional probability distribution across subjects. The key feature of the first framework is a novel weighting

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)Association for Computing Machinery

Published: Dec 1, 2012

References