Dynamic muscle fatigue detection using self-organizing maps

Authors:
Dimitrios Moshou;Ivo Hostens;George Papaioannou;Herman Ramon
Affiliations:
Department of Agro-Engineering and Economics, Laboratory for Agro-Machinery and Processing, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium;Department of Agro-Engineering and Economics, Laboratory for Agro-Machinery and Processing, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium;Department of Biomedical Engineering, The Catholic University of America, 620 Michigan Avenue N.E., Washington, DC 20064, USA;Department of Agro-Engineering and Economics, Laboratory for Agro-Machinery and Processing, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium
Venue:
Applied Soft Computing
Year:
2005

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Abstract

Wavelets are used for the processing of signals that are non-stationary and time varying. The electromyogram (EMG) contains transient signals related to muscle activity. Wavelet coefficients are proposed as features for identifying muscle fatigue. By observing the approximation coefficients it is shown that their amplitude follows closely the muscle fatigue development. The proposed method for detecting fatigue is automated by using neural networks. The self-organizing map (SOM) has been used to visualize the variation of the approximation wavelet coefficients and aid the detection of muscle fatigue. The results show that a 2D SOM separates EMG signatures from fresh and fatigued muscles, thus providing a visualization of the onset of fatigue over time. The map is able to detect if muscles have recovered temporarily. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.