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Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise
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 oxyg...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4570431/ https://www.ncbi.nlm.nih.gov/pubmed/26295396 http://dx.doi.org/10.3390/s150820480 |
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author | Ražanskas, Petras Verikas, Antanas Olsson, Charlotte Viberg, Per-Arne |
author_facet | Ražanskas, Petras Verikas, Antanas Olsson, Charlotte Viberg, Per-Arne |
author_sort | Ražanskas, Petras |
collection | PubMed |
description | 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 [Formula: see text] to [Formula: see text] (for blood lactate) and from [Formula: see text] to [Formula: see text] (for oxygen uptake) were obtained when using random forest regressors. |
format | Online Article Text |
id | pubmed-4570431 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-45704312015-09-17 Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise Ražanskas, Petras Verikas, Antanas Olsson, Charlotte Viberg, Per-Arne Sensors (Basel) Article 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 [Formula: see text] to [Formula: see text] (for blood lactate) and from [Formula: see text] to [Formula: see text] (for oxygen uptake) were obtained when using random forest regressors. MDPI 2015-08-19 /pmc/articles/PMC4570431/ /pubmed/26295396 http://dx.doi.org/10.3390/s150820480 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ražanskas, Petras Verikas, Antanas Olsson, Charlotte Viberg, Per-Arne Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise |
title | Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise |
title_full | Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise |
title_fullStr | Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise |
title_full_unstemmed | Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise |
title_short | Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise |
title_sort | predicting blood lactate concentration and oxygen uptake from semg data during fatiguing cycling exercise |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4570431/ https://www.ncbi.nlm.nih.gov/pubmed/26295396 http://dx.doi.org/10.3390/s150820480 |
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