2011 MultiSourceDomainAdaptationandI

• (Chattopadhyay et al., 2011) ⇒ Rita Chattopadhyay, Jieping Ye, Sethuraman Panchanathan, Wei Fan, and Ian Davidson. (2011). “Multi-source Domain Adaptation and Its Application to Early Detection of Fatigue.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020520

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Multi-source+Domain+Adaptation+and+Its+Application+to+Early+Detection+of+Fatigue
• http://dl.acm.org/citation.cfm?id=2020408.2020520&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; data mining; multi-source domain adaption; subject based variability; surface electromyogram; transfer learning

Abstract

We consider the characterization of muscle fatigue through 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 paper, we propose a transfer learning framework based on the multi-source domain adaptation methodology for detecting different stages of fatigue using SEMG signals, that addresses the distribution differences. In the proposed framework, 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 proposed framework is a novel weighting scheme that addresses the conditional probability distribution differences across multiple domains (subjects). We have validated the proposed framework on Surface Electromyogram signals collected from 8 people during a fatigue-causing repetitive gripping activity. Comprehensive experiments on the SEMG data set demonstrate that the proposed method improves the classification accuracy by 20% to 30% over the cases without any domain adaptation method and by 13% to 30% over the existing state-of-the-art domain adaptation methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 MultiSourceDomainAdaptationandI	Wei Fan Jieping Ye Ian Davidson Rita Chattopadhyay Sethuraman Panchanathan			Multi-source Domain Adaptation and Its Application to Early Detection of Fatigue				10.1145/2020408.2020520		2011