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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6864433 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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