hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-0878-8130
Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS). Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-2513-3040
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).ORCID iD: 0000-0002-9337-5113
Show others and affiliations
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 21, article id 4729Article in journal (Refereed) Published
Abstract [en]

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. © 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/).

Place, publisher, year, edition, pages
Basel: MDPI, 2019. Vol. 19, no 21, article id 4729
Keywords [en]
surface-electromyography, blood lactate concentration, random forest, running, fatigue
National Category
Sport and Fitness Sciences
Identifiers
URN: urn:nbn:se:hh:diva-40834DOI: 10.3390/s19214729Scopus ID: 2-s2.0-85074441602OAI: oai:DiVA.org:hh-40834DiVA, id: diva2:1367497
Funder
Knowledge Foundation
Note

Other funder: Swedish Adrenaline.

Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-12-06Bibliographically approved

Open Access in DiVA

fulltext(6313 kB)1 downloads
File information
File name FULLTEXT01.pdfFile size 6313 kBChecksum SHA-512
2bdf9a8c381b24a4718905d231ddc77b065d68ea6204a8545b32e3d816919976e66be8fda8047957507d1842cbc22566a0710b01f37172b12302202254991ef2
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Khan, TahaLundgren, LinaJärpe, EricOlsson, M. Charlotte

Search in DiVA

By author/editor
Khan, TahaLundgren, LinaJärpe, EricOlsson, M. Charlotte
By organisation
CAISR - Center for Applied Intelligent Systems ResearchThe Rydberg Laboratory for Applied Sciences (RLAS)
In the same journal
Sensors
Sport and Fitness Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 1 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 15 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf