Statistical class separation using sEMG features towards automated muscle fatigue detection and prediction

M. R. Al-Mulla, F. Sepulveda, M. Colley, Fahd Al-Mulla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

Surface Electromyography (sEMG) activity of the biceps muscle was recorded from ten subjects. Data were recorded while subjects performed isometric contraction until fatigue. The signals were segmented into three parts (Non-Fatigue, Transition-to-Fatigue and Fatigue), assisted by a fuzzy classifier using arm angle and arm oscillation as inputs. Nine features were extracted from each of the three classes to quantify the potential performance of each feature, also aiding towards the differentiation of the three classes of muscle fatigue within the sEMG signal. Percent change was calculated between Non-Fatigue and Transition-to-Fatigue and also between Transition-to-Fatigue and Fatigue classes. Estimation of relative class overlap using Partition Index approach was used to show features that can best distinguish between the three classes and quantifying class separability. Results show that the selected dominant frequency best discriminate between the classes, giving the highest average percent change of 159.37% and 64.75%. Partition Index showed small values confirming the percent change calculations.

Original languageEnglish
Title of host publicationProceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09
DOIs
Publication statusPublished - 1 Dec 2009
Event2009 2nd International Congress on Image and Signal Processing, CISP'09 - Tianjin, China
Duration: 17 Oct 200919 Oct 2009

Publication series

NameProceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09

Conference

Conference2009 2nd International Congress on Image and Signal Processing, CISP'09
CountryChina
CityTianjin
Period17/10/0919/10/09

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Keywords

  • Classification
  • Component
  • Electromyography
  • Feature extraction
  • Muscle fatigue
  • Transition-to-fatigue

Cite this

Al-Mulla, M. R., Sepulveda, F., Colley, M., & Al-Mulla, F. (2009). Statistical class separation using sEMG features towards automated muscle fatigue detection and prediction. In Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09 [5304091] (Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09). https://doi.org/10.1109/CISP.2009.5304091