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* ([[2011_MultiSourceDomainAdaptationandI|Chattopadhyay | * ([[2011_MultiSourceDomainAdaptationandI|Chattopadhyay et al., 2011]]) ⇒ [[author::Rita Chattopadhyay]], [[author::Jieping Ye]], [[author::Sethuraman Panchanathan]], [[author::Wei Fan]], and [[author::Ian Davidson]]. ([[year::2011]]). “Multi-source Domain Adaptation and Its Application to Early Detection of Fatigue.” In: [[proceedings::Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[conference::KDD-2011]]) Journal. ISBN:978-1-4503-0813-7 [http://dx.doi.org/10.1145/2020408.2020520 doi:10.1145/2020408.2020520] | ||
<B>Subject Headings:</B> | <B>Subject Headings:</B> | ||
==Notes== | == Notes == | ||
==Cited By== | ==Cited By== | ||
* http://scholar.google.com/scholar?q=% | * http://scholar.google.com/scholar?q=%222011%22+Multi-source+Domain+Adaptation+and+Its+Application+to+Early+Detection+of+Fatigue | ||
* http:// | * http://dl.acm.org/citation.cfm?id=2020408.2020520&preflayout=flat#citedby | ||
==Quotes | == Quotes == | ||
=== | === Author Keywords === | ||
* [[Algorithm]]s; [[data mining]]; [[multi-source domain adaption]]; [[subject based variability]]; [[surface electromyogram]]; [[transfer learning]] | |||
==References== | === Abstract === | ||
[[We]] consider the [[characterization]] of [[muscle fatigue]] through [[noninvasive sensing mechanism]] such as [[surface electromyography (SEMG)]]. </s> | |||
While changes in the [[properti]]es of [[SEMG signal]]s with respect to [[muscle fatigue]] have been reported in the [[literature]], the large [[variation]] in [[SEMG signal|these signal]]s across different [[individual]]s makes the [[task of modeling]] and [[classification]] of [[SEMG signal]]s challenging. </s> | |||
Indeed, the [[variation]] in [[SEMG parameter]]s from [[test subject|subject]] to [[test subject|subject]] creates differences in the [[data distribution]]. </s> | |||
[[In this paper, we]] propose a [[transfer learning framework]] based on the [[multi-source domain adaptation methodology]] for [[detect]]ing different [[stages of fatigue]] using [[SEMG signal]]s, that addresses the [[distribution]] [[difference]]s. </s> | |||
In [[the proposed framework]], the [[SEMG data]] of a [[test subject|subject]] represent a [[domain]]; [[data]] from multiple [[subject]]s in the [[training set]] form the [[multiple source domain]]s and the [[test subject data]] form the [[target domain]]. </s> | |||
[[SEMG signal]]s are predominantly different in [[conditional probability distribution]] across [[test subject|subject]]s. </s> | |||
The key feature of [[the proposed framework]] is a novel [[weighting scheme]] that addresses the [[conditional probability distribution]] [[difference]]s across [[multiple domain]]s ([[test subject|subject]]s). </s> | |||
[[We]] have validated [[the proposed framework]] on [[Surface Electromyogram signal]]s collected from 8 [[people]] during a [[fatigue-causing repetitive gripping activity]]. </s> | |||
Comprehensive [[experiment]]s on the [[SEMG data set]] demonstrate that the [[proposed method]] improves the [[classification]] [[accuracy]] by 20% to 30% over the [[case]]s without any [[domain adaptation method]] and by 13% to 30% over the existing [[state-of-the-art]] [[domain adaptation method]]s. </s> | |||
== References == | |||
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Latest revision as of 21:40, 2 December 2023
- (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:
Notes
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
;
| 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 |