Abstract

A multi source domain adaptation based learning for addressing subject based variability in myoelectric signals (SEMG), enabling generalized framework for detecting stages of fatigue.

Original languageEnglish (US)
Title of host publicationComputational Physiology - Papers from the AAAI Spring Symposium, Technical Report
Pages10-12
Number of pages3
StatePublished - Aug 15 2011
Event2011 AAAI Spring Symposium - Stanford, CA, United States
Duration: Mar 21 2011Mar 23 2011

Publication series

NameAAAI Spring Symposium - Technical Report
VolumeSS-11-04

Other

Other2011 AAAI Spring Symposium
CountryUnited States
CityStanford, CA
Period3/21/113/23/11

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Chattopadhyay, R., Ye, J., & Panchanathan, S. (2011). Transfer learning framework for early detection of fatigue using noninvasive surface electromyogram signals (SEMG). In Computational Physiology - Papers from the AAAI Spring Symposium, Technical Report (pp. 10-12). (AAAI Spring Symposium - Technical Report; Vol. SS-11-04).