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Sensors 2015, 15(8), 20480-20500; doi:10.3390/s150820480

Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise

1
Department of Electric Power Systems, Kaunas University of Technology, Studentų g. 50, Kaunas LT-51368, Lithuania
2
Intelligent Systems Laboratory, Halmstad University, P.O. Box 823, Halmstad S-30118, Sweden
3
Biological and Environmental Systems Laboratory, Halmstad University, P.O. Box 823, Halmstad S-30118, Sweden
4
Swedish Adrenaline, Pilefeltsgatan 73, S-30250 Halmstad, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: W. Rudolf Seitz
Received: 23 April 2015 / Revised: 22 July 2015 / Accepted: 5 August 2015 / Published: 19 August 2015
(This article belongs to the Section Chemical Sensors)
View Full-Text   |   Download PDF [472 KB, uploaded 19 August 2015]   |  

Abstract

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. View Full-Text
Keywords: blood lactate concentration; cycling; surface electromyography; oxygen uptake;random forest; ridge regression blood lactate concentration; cycling; surface electromyography; oxygen uptake;random forest; ridge regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ražanskas, P.; Verikas, A.; Olsson, C.; Viberg, P.-A. Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise. Sensors 2015, 15, 20480-20500.

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