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A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes...

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Autores principales: Khan, Taha, Lundgren, Lina E., Järpe, Eric, Olsson, M. Charlotte, Viberg, Pelle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864433/
https://www.ncbi.nlm.nih.gov/pubmed/31683532
http://dx.doi.org/10.3390/s19214729
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author Khan, Taha
Lundgren, Lina E.
Järpe, Eric
Olsson, M. Charlotte
Viberg, Pelle
author_facet Khan, Taha
Lundgren, Lina E.
Järpe, Eric
Olsson, M. Charlotte
Viberg, Pelle
author_sort Khan, Taha
collection PubMed
description Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running.
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spelling pubmed-68644332019-12-23 A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation Khan, Taha Lundgren, Lina E. Järpe, Eric Olsson, M. Charlotte Viberg, Pelle Sensors (Basel) Article Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running. MDPI 2019-10-31 /pmc/articles/PMC6864433/ /pubmed/31683532 http://dx.doi.org/10.3390/s19214729 Text en © 2019 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Khan, Taha
Lundgren, Lina E.
Järpe, Eric
Olsson, M. Charlotte
Viberg, Pelle
A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
title A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
title_full A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
title_fullStr A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
title_full_unstemmed A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
title_short A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
title_sort novel method for classification of running fatigue using change-point segmentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864433/
https://www.ncbi.nlm.nih.gov/pubmed/31683532
http://dx.doi.org/10.3390/s19214729
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