Scalable weighted-cumulated methodology for fatigue estimation
Author:
Funder
Fundação de Apoio à Pesquisa do Distrito Federal
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42600-022-00241-z.pdf
Reference48 articles.
1. Al-Mulla MR, Sepulveda F, Colley M. A review of non-invasive techniques to detect and predict localized muscle fatigue. Sensors. 2011;11:3545–94. https://doi.org/10.3390/s110403545.
2. Arabadzhiev TI, Dimitrov VG, Dimitrova NA, Dimitrov GV. Interpretation of EMG integral or RMS and estimates of “neuromuscular efficiency” can be misleading in fatiguing contraction. J Electromyogr Kinesiol. 2010;20:223–32.
3. Biagetti G, Crippa P, Curzi A, Orcioni S, Turchetti C. Analysis of the EMG signal during cyclic movements using multicomponent AM–FM decomposition. IEEE J Biomed Health Inform. 2015;19(5):1672–81.
4. Bigliassi M, Scalassara PR, Kanthack TFD, Abrão T, de Moraes AC, Altimari LR. Fourier and wavelet spectral analysis of EMG signals in 1-km cycling time-trial. Appl Math. 2014;5:1878–86.
5. Chowdhury SK, Nimbarte AD, Jaridi M, Creese RC. Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles. J Electromyogr Kinesiol. 2013;23(5):995–1003.
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