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Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing
Department of Electric Power Systems, 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). Department of Electrical Power Systems, Kaunas University of Technology, Lithuania.ORCID-id: 0000-0003-2185-8973
Swedish Adrenaline, Halmstad, Sweden.
Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Rydberglaboratoriet för tillämpad naturvetenskap (RLAS).ORCID-id: 0000-0002-9337-5113
2017 (Engelska)Ingår i: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 35, s. 19-29Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This article is concerned with a novel technique for prediction of blood lactate concentration level and oxygen uptake rate from multi-channel surface electromyography (sEMG) signals. The approach is built on predictive models exploiting a set of novel time-domain variables computed from sEMG signals. Signals from three muscles of each leg, namely, vastus lateralis, rectus femoris, and semitendinosus were used in this study. The feature set includes parameters reflecting asymmetry between legs, phase shifts between activation of different muscles, active time percentages, and sEMG amplitude. Prediction ability of both linear and non-linear (random forests-based) models was explored. The random forests models showed very good prediction accuracy and attained the coefficient of determination R2 = 0.962 for lactate concentration level and R2 = 0.980 for oxygen uptake rate. The linear models showed lower prediction accuracy. Comparable results were obtained also when sEMG amplitude data were removed from the training sets. A feature elimination algorithm allowed to build accurate random forests (R2 > 0.9) using just six (lactate concentration level) or four (oxygen uptake rate) time-domain variables. Models created to predict blood lactate concentration rate relied on variables reflecting interaction between front and back leg muscles, while parameters computed from front muscles and interactions between two legs were the most important variables for models created to predict oxygen uptake rate.© 2017 Elsevier Ltd.

Ort, förlag, år, upplaga, sidor
Amsterdam: Elsevier, 2017. Vol. 35, s. 19-29
Nyckelord [en]
Random forests, Surface electromyography, Muscle activation patterns, Fatigue detection, Bicycling
Nationell ämneskategori
Medicinteknik
Identifikatorer
URN: urn:nbn:se:hh:diva-33966DOI: 10.1016/j.bspc.2017.02.011ISI: 000401209300003Scopus ID: 2-s2.0-85014392704OAI: oai:DiVA.org:hh-33966DiVA, id: diva2:1105430
Tillgänglig från: 2017-06-03 Skapad: 2017-06-03 Senast uppdaterad: 2017-06-09Bibliografiskt granskad

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Verikas, AntanasOlsson, Charlotte M.

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CAISR Centrum för tillämpade intelligenta system (IS-lab)Rydberglaboratoriet för tillämpad naturvetenskap (RLAS)
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Biomedical Signal Processing and Control
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