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A Novel Method for ...
A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
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- Khan, Taha, 1983- (författare)
- Högskolan i Halmstad,CAISR Centrum för tillämpade intelligenta system (IS-lab)
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- Lundgren, Lina, 1982- (författare)
- Högskolan i Halmstad,Rydberglaboratoriet för tillämpad naturvetenskap (RLAS),CAISR Centrum för tillämpade intelligenta system (IS-lab)
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- Järpe, Eric, 1965- (författare)
- Högskolan i Halmstad,CAISR Centrum för tillämpade intelligenta system (IS-lab)
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- Olsson, M. Charlotte, 1967- (författare)
- Högskolan i Halmstad,Rydberglaboratoriet för tillämpad naturvetenskap (RLAS)
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- Wiberg, Pelle (författare)
- Raytelligence AB, Halmstad, Sweden
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(creator_code:org_t)
- 2019-10-31
- 2019
- Engelska.
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Ingår i: Sensors. - Basel : MDPI. - 1424-8220. ; 19:21
- Relaterad länk:
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https://doi.org/10.3...
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https://hh.diva-port... (primary) (Raw object)
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https://www.mdpi.com...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- 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/).
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Idrottsvetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Sport and Fitness Sciences (hsv//eng)
Nyckelord
- surface-electromyography
- blood lactate concentration
- random forest
- running
- fatigue
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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