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Open AccessArticle

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

1
Centre of Artificial Intelligence, School of information technology, Halmstad University, SE-301 18 Halmstad, Sweden
2
Rydberg Laboratory of Applied Science, School of business, engineering and science, Halmstad University, SE-301 18 Halmstad, Sweden
3
Raytelligence AB, 302 42 Halmstad, Sweden
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(21), 4729; https://doi.org/10.3390/s19214729
Received: 11 September 2019 / Revised: 14 October 2019 / Accepted: 29 October 2019 / Published: 31 October 2019
(This article belongs to the Special Issue Sensors for Biomechanics Application)
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. View Full-Text
Keywords: surface-electromyography; blood lactate concentration; random forest; running; fatigue surface-electromyography; blood lactate concentration; random forest; running; fatigue
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MDPI and ACS Style

Khan, T.; Lundgren, L.E.; Järpe, E.; Olsson, M.C.; Viberg, P. A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation. Sensors 2019, 19, 4729.

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