{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T03:51:24Z","timestamp":1771559484642,"version":"3.50.1"},"reference-count":20,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T00:00:00Z","timestamp":1572480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003170","name":"Stiftelsen f\u00f6r Kunskaps- och Kompetensutveckling","doi-asserted-by":"publisher","award":["CAISR"],"award-info":[{"award-number":["CAISR"]}],"id":[{"id":"10.13039\/501100003170","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 &lt; 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running.<\/jats:p>","DOI":"10.3390\/s19214729","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T06:33:29Z","timestamp":1572503609000},"page":"4729","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0878-8130","authenticated-orcid":false,"given":"Taha","family":"Khan","sequence":"first","affiliation":[{"name":"Centre of Artificial Intelligence, School of information technology, Halmstad University, SE-301 18 Halmstad, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2513-3040","authenticated-orcid":false,"given":"Lina E.","family":"Lundgren","sequence":"additional","affiliation":[{"name":"Centre of Artificial Intelligence, School of information technology, Halmstad University, SE-301 18 Halmstad, Sweden"},{"name":"Rydberg Laboratory of Applied Science, School of business, engineering and science, Halmstad University, SE-301 18 Halmstad, Sweden"}]},{"given":"Eric","family":"J\u00e4rpe","sequence":"additional","affiliation":[{"name":"Centre of Artificial Intelligence, School of information technology, Halmstad University, SE-301 18 Halmstad, Sweden"}]},{"given":"M. Charlotte","family":"Olsson","sequence":"additional","affiliation":[{"name":"Rydberg Laboratory of Applied Science, School of business, engineering and science, Halmstad University, SE-301 18 Halmstad, Sweden"}]},{"given":"Pelle","family":"Viberg","sequence":"additional","affiliation":[{"name":"Raytelligence AB, 302 42 Halmstad, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1139\/h03-023","article-title":"Anaerobic threshold: The concept and methods of measurement","volume":"28","author":"Svedahl","year":"2003","journal-title":"Can. J. Appl. Physiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.proeng.2015.07.193","article-title":"Muscle activity analysis with a smart compression garment","volume":"112","author":"Belbasis","year":"2015","journal-title":"Procedia Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.jelekin.2016.08.002","article-title":"Detecting fatigue thresholds from electromyographic signals: A systematic review of approaches and methodologies","volume":"30","author":"Ertl","year":"2016","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_4","first-page":"146","article-title":"Validation of the physical working capacity at the fatigue threshold treadmill test","volume":"49","author":"Camic","year":"2017","journal-title":"Kinesiol. Int. J. Fundam. Appl. Kinesiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jelekin.2010.09.002","article-title":"The relationship between blood potassium, blood lactate, and electromyography signals related to fatigue in a progressive cycling exercise test","volume":"21","author":"Tenan","year":"2011","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.3390\/s110201542","article-title":"An autonomous wearable system for predicting and detecting localized muscle fatigue","volume":"11","author":"Sepulveda","year":"2011","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.cmpb.2017.10.024","article-title":"Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms","volume":"154","author":"Karthick","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.bspc.2017.02.011","article-title":"Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing","volume":"35","author":"Verikas","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"469","DOI":"10.2165\/00007256-200939060-00003","article-title":"Lactate threshold concepts","volume":"39","author":"Faude","year":"2009","journal-title":"Sports Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pallar\u00e9s, J.G., Mor\u00e1n-Navarro, R., Ortega, J.F., Fern\u00e1ndez-El\u00edas, V.E., and Mora-Rodriguez, R. (2016). Validity, and reliability of ventilatory and blood lactate thresholds in well-trained cyclists. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0163389"},{"key":"ref_11","first-page":"536","article-title":"On the theory of filter amplifiers","volume":"7","author":"Butterworth","year":"1930","journal-title":"Wirel. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20480","DOI":"10.3390\/s150820480","article-title":"Predicting blood lactate concentration and oxygen uptake from sEMG data during fatiguing cycling exercise","volume":"15","author":"Verikas","year":"2015","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Catmull, E., and Rom, R. (1974). A class of local interpolating splines. Computer Aided Geometric Design, Academic Press.","DOI":"10.1016\/B978-0-12-079050-0.50020-5"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cutler, A., Cutler, D.R., and Stevens, J.R. (2012). Random forests. Ensemble Machine Learning, Springer.","DOI":"10.1007\/978-1-4419-9326-7_5"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Friedman, J., Hastie, T., and Tibshirani, R. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1109\/10.930899","article-title":"Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions","volume":"48","author":"Bonato","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","first-page":"73","article-title":"Determination of the individual anaerobic threshold","volume":"21","author":"Bunc","year":"1985","journal-title":"Acta Univ. Carol. Gymnica"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1249\/01.mss.0000535953.85249.2f","article-title":"Evaluating the Influence of Methodological Variables on the Determination of Vo2max and the Lactate Threshold","volume":"50","author":"Jamnick","year":"2018","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1249\/00005768-198910000-00012","article-title":"Longitudinal assessment of responses by triathletes to swimming, cycling, and running","volume":"21","author":"Kohrt","year":"1989","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/JBHI.2012.2236563","article-title":"Source selection for real-time user intent recognition toward volitional control of artificial legs","volume":"17","author":"Zhang","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4729\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:30:42Z","timestamp":1760189442000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4729"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,31]]},"references-count":20,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19214729"],"URL":"https:\/\/doi.org\/10.3390\/s19214729","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,31]]}}}