Paper Info
Title
Multisource domain adaptation and its application to early detection of fatigue
Abstract
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 scheme that addresses the conditional probability distribution differences across multiple domains (subjects) and the key feature of the second framework is a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data. The weights for minimizing the marginal probability differences are estimated independently, while the weights for minimizing conditional probability differences are computed simultaneously by exploiting the potential interaction among multiple sources. We also provide a theoretical analysis on the generalization performance of the proposed multisource domain adaptation formulation using the weighted Rademacher complexity measure. We have validated the proposed frameworks on Surface ElectroMyoGram signals collected from 8 people during a fatigue-causing repetitive gripping activity. Comprehensive experiments on the SEMG dataset 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 existing state-of-the-art domain adaptation methods.
Year
DOI
Venue
2012
10.1145/2382577.2382582
TKDD
Keywords
DocType
Volume
proposed framework,early detection,key feature,muscle fatigue,domain adaptation method,multisource domain adaptation,marginal probability difference,semg parameter,conditional probability difference,semg signal,multiple source,semg data,transfer learning
Journal
6
Issue
ISSN
Citations 
4
1556-4681
23
PageRank 
References 
Authors
0.73
16
6
Name
Order
Citations
PageRank
Rita Chattopadhyay11176.55
Qian Sun21827.39
Wei Fan34205253.58
Ian Davidson4127477.79
Sethuraman Panchanathan51431152.04
Jieping Ye66943351.37