hh.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Time Domain Features of Multi-channel EMG Applied to Prediction of Physiological Parameters in Fatiguing Bicycling Exercises
Kaunas University of Technology, Kaunas, Lithuania.
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0003-2185-8973
Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).ORCID-id: 0000-0002-9337-5113
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
2015 (Engelska)Ingår i: Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314, Vol. 278, s. 118-127Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

A set of novel time-domain features characterizing multi-channel surface EMG (sEMG) signals of six muscles (rectus femoris, vastus lateralis, and semitendinosus of each leg) is proposed for prediction of physiological parameters considered important in cycling: blood lactate concentration and oxygen uptake. Fifty one different features, including phase shifts between muscles, active time percentages, sEMG amplitudes, as well as symmetry measures between both legs, were defined from sEMG data and used to train linear and random forest models. The random forests models achieved the coefficient of determination R2 = 0:962 (lactate) and R2 = 0:980 (oxygen). The linear models were less accurate. Feature pruning applied enabled creating accurate random forest models (R2 >0:9) using as few as 7 (lactate) or 4 (oxygen) time-domain features. sEMG amplitude was important for both types of models. Models to predict lactate also relied on measurements describing interaction between front and back muscles, while models to predict oxygen uptake relied on front muscles only, but also included interactions between the two legs. © 2015 The authors and IOS Press. All rights reserved.

Ort, förlag, år, upplaga, sidor
Amsterdam: IOS Press, 2015. Vol. 278, s. 118-127
Nyckelord [en]
random forests, electromyography, muscle activation patterns, fatigue detection, bicycling
Nationell ämneskategori
Medicinteknik
Identifikatorer
URN: urn:nbn:se:hh:diva-29666DOI: 10.3233/978-1-61499-589-0-118ISI: 000455950400014Scopus ID: 2-s2.0-84963682719OAI: oai:DiVA.org:hh-29666DiVA, id: diva2:862405
Tillgänglig från: 2015-10-22 Skapad: 2015-10-22 Senast uppdaterad: 2020-02-03Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Person

Verikas, AntanasOlsson, CharlotteWiberg, Per-Arne

Sök vidare i DiVA

Av författaren/redaktören
Verikas, AntanasOlsson, CharlotteWiberg, Per-Arne
Av organisationen
CAISR Centrum för tillämpade intelligenta system (IS-lab)Bio- och miljösystemforskning (BLESS)
I samma tidskrift
Frontiers in Artificial Intelligence and Applications
Medicinteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 305 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf