{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T01:25:03Z","timestamp":1769304303838,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2010,5,12]],"date-time":"2010-05-12T00:00:00Z","timestamp":1273622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface Electromyography (sEMG) activity of the biceps muscle was recorded from ten subjects performing isometric contraction until fatigue. A novel feature (1D spectro_std) was used to extract the feature that modeled three classes of fatigue, which enabled the prediction and detection of fatigue. Initial results of class separation were encouraging, discriminating between the three classes of fatigue, a longitudinal classification on Non-Fatigue and Transition-to-Fatigue shows 81.58% correct classification with accuracy 0.74 of correct predictions while the longitudinal classification on Transition-to-Fatigue and Fatigue showed lower average correct classification of 66.51% with a positive classification accuracy 0.73 of correct prediction. Comparison of the 1D spectro_std with other sEMG fatigue features on the same dataset show a significant improvement in classification, where results show a significant 20.58% (p &lt; 0.01) improvement when using the 1D spectro_std to classify Non-Fatigue and Transition-to-Fatigue. In classifying Transition-to-Fatigue and Fatigue results also show a significant improvement over the other features giving 8.14% (p &lt; 0.05) on average of all compared features.<\/jats:p>","DOI":"10.3390\/s100504838","type":"journal-article","created":{"date-parts":[[2010,5,12]],"date-time":"2010-05-12T11:04:38Z","timestamp":1273662278000},"page":"4838-4854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Novel Feature Modelling the Prediction and Detection of sEMG Muscle Fatigue towards an Automated Wearable System"],"prefix":"10.3390","volume":"10","author":[{"given":"Mohamed R.","family":"Al-Mulla","sequence":"first","affiliation":[{"name":"School of Computer Science and Electronic Engineering, University of Essex-Wivenhoe Park, Colchester CO4 3SQ, UK"}]},{"given":"Francisco","family":"Sepulveda","sequence":"additional","affiliation":[{"name":"School of Computer Science and Electronic Engineering, University of Essex-Wivenhoe Park, Colchester CO4 3SQ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2010,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1007\/BF00241654","article-title":"Quantification of Erector Spinae Muscle Fatigue during Prolonged, Dynamic Lifting Tasks","volume":"67","author":"Potvin","year":"1993","journal-title":"Euro. J. Appl. Physiol"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3233\/IFS-2009-0411","article-title":"Robust EMG Pattern Recognition to Muscular Fatigue Effect for Human- Machine Interaction","volume":"20","author":"Song","year":"2009","journal-title":"J. Intell. Fuzzy. Syst"},{"key":"ref_3","unstructured":"Asghari Oskoei, M., Huosheng, H., and Gan, J.Q. (, January August). Manifestation of Fatigue in Myoelectric Signals of Dynamic Contractions Produced During Playing PC Games. Vancouver, Canada."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/00140138208924942","article-title":"Evaluation of the Amplitude and Frequency Components of the Surface EMG as an Index of Muscle Fatigue","volume":"25","author":"Petrofesky","year":"1982","journal-title":"Ergon"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/00140138108924875","article-title":"Work Load and Fatigue in Repetitive Arm Elevations","volume":"24","author":"Hagberg","year":"1981","journal-title":"Ergon"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TNSRE.2003.819901","article-title":"Wavelet Analysis of Surface Electromyography to Determine Muscle Fatigue","volume":"11","author":"Kumar","year":"2003","journal-title":"IEEE Trans. Neural. Syst. Rehabil. Eng"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1682\/JRRD.2007.11.0196","article-title":"Gender Differences in Spectral and Entropic Measures of Erector Spinae Muscle Fatigue","volume":"45","author":"Sung","year":"2008","journal-title":"J. Rehabil. Res. Dev"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.jelekin.2004.08.008","article-title":"An EMG Fractal Indicator Having Different Sensitivities to Changes in Force and Muscle Fatigue during Voluntary Static Muscle Contractions","volume":"15","author":"Ravier","year":"2005","journal-title":"J. Electromyogr. Kinesiol"},{"key":"ref_9","unstructured":"Yassierli, N. (2008, January 7). Assessment of Localized Muscle Fatigue for Industrial Task Evaluations. Medan, Indonesia."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1631\/jzus.2007.A0910","article-title":"Multifractal Analysis of Surface EMG Signals for Assessing Muscle Fatigue during Static Contractions","volume":"8","author":"Wang","year":"2007","journal-title":"J. Zhejiang Uni. Sci. A"},{"key":"ref_11","unstructured":"Filligoi, G., Felici, F., Vicini, M., and Rosponi, A. Recurrence Quantification Analysis of Surface Electromyograms. Available online: http:\/\/library.med.utah.edu\/cyprus\/proceedings\/medicon98\/medicon98.filligoi.giancarlo.pdf\/ (accessed on 12 February 2010)."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.jneumeth.2008.09.023","article-title":"Recurrence Quantification Analysis of Surface Electromyographic Signal: Sensitivity to Potentiation and Neuromuscular Fatigue","volume":"177","author":"Morana","year":"2009","journal-title":"J. Neurosci. Meth"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.ergon.2004.08.011","article-title":"The Sensitivity of Autoregressive Model Coefficient in Quantification of Trunk Muscle Fatigue during a Sustained Isometric Contraction","volume":"35","author":"Kim","year":"2005","journal-title":"Int. J. Ind. Ergonomic"},{"key":"ref_14","unstructured":"Al-Mulla, M.R., Sepulveda, F., Colley, M., and Kattan, A. (, January September). Classification of Localized Muscle Fatigue with Genetic Programming on sEMG During Isometric Contraction. Minneapolis, MN, USA."},{"key":"ref_15","unstructured":"Al-Mulla, M.R., Sepulveda, F., and Colley, M. (, January October). Statistical Class Separation Using sEMG Features towards Automated Muscle Fatigue Detection and Prediction. Tjianjin, China."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Al-Mulla, M.R., and Sepulveda, F. (2010, January 28). Novel Feature Assisting in The Prediction of sEMG Muscle Fatigue towards Online Prediction. La Grande Motte, France. (in press).","DOI":"10.1109\/IMS3TW.2010.5503001"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1251\/bpo115","article-title":"Techniques of EMG Signal Snalysis: Detection, Processing, Classification and Applications","volume":"8","author":"Raez","year":"2006","journal-title":"Biol. Proced. Online"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/10.821766","article-title":"Enhancement of Spectral Analysis of Myoelectric Signals during Dynamic Contractions: A Comparative Study","volume":"46","author":"Karlsson","year":"2000","journal-title":"IEEE. Trans. BME"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/S1050-6411(98)00010-8","article-title":"EMG Assessment of Back Muscle Function During Cyclical Lifting","volume":"8","author":"Roy","year":"1998","journal-title":"J. Electromyogr Kinesiol"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.clinbiomech.2009.01.010","article-title":"Surface EMG Based Muscle Fatigue Evaluation in Biomechanics","volume":"24","author":"Cifrek","year":"2009","journal-title":"Clin. Biomech"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The Use of Multiple Measurements in Taxonomic Problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1249\/01.mss.0000233794.31659.6d","article-title":"Muscle Fatigue During Dynamic Contractions Assessed by New Spectral Indices","volume":"38","author":"Dimitrov","year":"2006","journal-title":"Med. Sci. Sport Exerc"},{"key":"ref_23","unstructured":"Walker, J.S. (1999). A Primer on Wavelets and their Scientific Applications, Chapman & Hall\/CRC Press."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1023\/A:1017181826899","article-title":"Glossary of Terms","volume":"30","author":"Kohavi","year":"1998","journal-title":"Mach. Learn"},{"key":"ref_25","unstructured":"Sepulveda, F., Meckes, M., and Conway, B. (2004, January 1\u20133). Cluster Separation Index Suggests Usefulness of Non-Motor Eeg Channels in Detecting Wrist Movement Di-Rection Intention. Singapore."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/10\/5\/4838\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:02:26Z","timestamp":1760220146000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/10\/5\/4838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,5,12]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2010,5]]}},"alternative-id":["s100504838"],"URL":"https:\/\/doi.org\/10.3390\/s100504838","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,5,12]]}}}