A subject independent computational framework is one which does not require to be calibrated by the specific subject data to be ready to be used on the subject. The greatest challenge in developing such a framework is the variation in parameters across subjects which is termed as subject based variability. Spectral and amplitude variations in surface myoelectric signals (SEMG) are analyzed to determine the fatigue state of a muscle. But variations in the spectrum and magnitude of myoelectric signals across subjects cause variations in both marginal and conditional probability distributions in the features extracted across subjects, making it difficult to model the signal for any automated signal classification. However we observe that the manifold of the multidimensional SEMG data have an inherent similarity as the physiological state moves from no fatigue to fatigue state. In this paper we exploit this specific feature of the SEMG data and propose a domain adaptation technique that is based on intrinsic manifold of the data preserved in a low dimensional space, thus reducing the marginal probability differences between the subjects, followed by an instance selection methodology, based on similar conditional probabilities in the mapped domain. The proposed method provides significant improvement in subject independent accuracies compared to cases without any domain adaptation methods and also compared to other state-of-the-art domain adaptation methodologies.

Original languageEnglish (US)
Title of host publicationComputational Physiology - Papers from the AAAI Spring Symposium, Technical Report
PublisherAI Access Foundation
Number of pages6
ISBN (Print)9781577354963
StatePublished - 2011
Event2011 AAAI Spring Symposium - Stanford, CA, United States
Duration: Mar 21 2011Mar 23 2011

Publication series

NameAAAI Spring Symposium - Technical Report


Other2011 AAAI Spring Symposium
Country/TerritoryUnited States
CityStanford, CA

ASJC Scopus subject areas

  • Artificial Intelligence


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