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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.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Department of Electrical Power Systems, Kaunas University of Technology, Lithuania.ORCID iD: 0000-0003-2185-8973
Swedish Adrenaline, Halmstad, Sweden.
Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).ORCID iD: 0000-0002-9337-5113
2017 (English)In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 35, 19-29 p.Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2017. Vol. 35, 19-29 p.
Keyword [en]
Random forests, Surface electromyography, Muscle activation patterns, Fatigue detection, Bicycling
National Category
Medical Engineering
Identifiers
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: diva2:1105430
Available from: 2017-06-03 Created: 2017-06-03 Last updated: 2017-06-09Bibliographically approved

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Verikas, AntanasOlsson, Charlotte M.
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