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Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise
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. Kaunas University of Technology, Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).ORCID iD: 0000-0002-9337-5113
Swedish Adrenaline, Halmstad, Sweden.
2015 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, no 8, 20480-20500 p.Article in journal (Refereed) Published
Abstract [en]

This article presents a study of the relationship between electromyographic (EMG) signals from vastus lateralis, rectus femoris, biceps femoris and semitendinosus muscles, collected during fatiguing cycling exercises, and other physiological measurements, such as blood lactate concentration and oxygen consumption. In contrast to the usual practice of picking one particular characteristic of the signal, e.g., the median or mean frequency, multiple variables were used to obtain a thorough characterization of EMG signals in the spectral domain. Based on these variables, linear and non-linear (random forest) models were built to predict blood lactate concentration and oxygen consumption. The results showed that mean and median frequencies are sub-optimal choices for predicting these physiological quantities in dynamic exercises, as they did not exhibit significant changes over the course of our protocol and only weakly correlated with blood lactate concentration or oxygen uptake. Instead, the root mean square of the original signal and backward difference, as well as parameters describing the tails of the EMG power distribution were the most important variables for these models. Coefficients of determination ranging from R2 = 0:77 to R2 = 0:98 (for blood lactate) and from R2 = 0:81 to R2 = 0:97 (for oxygen uptake) were obtained when using random forest regressors.

Place, publisher, year, edition, pages
Basel: MDPI , 2015. Vol. 15, no 8, 20480-20500 p.
Keyword [en]
blood lactate concentration, cycling, surface electromyography, oxygen uptake, random forest, ridge regression
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-29246DOI: 10.3390/s150820480ISI: 000360906500135PubMedID: 26295396Scopus ID: 2-s2.0-84939817576OAI: oai:DiVA.org:hh-29246DiVA: diva2:847349
Funder
Knowledge Foundation
Note

The research was supported by the Research Council of Lithuania and the Knowledge foundation of Sweden through its research profile CAISR (Center for Applied Intelligent Systems Research). We would like to extend our thanks to Nicholas Wickström, Siddhartha Khandelwal and Björn Frandsen fortheir invaluable help during data collection and analysis.

Available from: 2015-08-20 Created: 2015-08-20 Last updated: 2017-08-18Bibliographically approved

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