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A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0003-0878-8130
Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Rydberglaboratoriet för tillämpad naturvetenskap (RLAS). Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-2513-3040
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Rydberglaboratoriet för tillämpad naturvetenskap (RLAS).ORCID-id: 0000-0002-9337-5113
Vise andre og tillknytning
2019 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, nr 21, artikkel-id 4729Artikkel i tidsskrift (Fagfellevurdert) 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/).

sted, utgiver, år, opplag, sider
Basel: MDPI, 2019. Vol. 19, nr 21, artikkel-id 4729
Emneord [en]
surface-electromyography, blood lactate concentration, random forest, running, fatigue
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-40834DOI: 10.3390/s19214729ISI: 000498834000126Scopus ID: 2-s2.0-85074441602OAI: oai:DiVA.org:hh-40834DiVA, id: diva2:1367497
Forskningsfinansiär
Knowledge Foundation
Merknad

Other funder: Swedish Adrenaline.

Tilgjengelig fra: 2019-11-04 Laget: 2019-11-04 Sist oppdatert: 2019-12-17bibliografisk kontrollert

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