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This paper aims to investigate problem of muscle fatigue through the
application of a comparative study by different processing techniques in order
to see the effect of physical exercise on Electromyography characteristics.
Indeed, electromyography is the best physiological examination to study muscle
activity and it is translated by an electromyogram (EMG).
The analysis of
the biomedical signal "EMG" before and after physical exercise
allowed us to quantify the physical effort and give some diagnostic elements
that can help the practitioner.
We applied some
signal processing techniques to quantify the physical effort and therefore we
were able to identify and detect muscle fatigue.
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J. M. Weiss, L.D Weiss and J.K. Silver, Easy EMG . Elsevier Inc., First edition, Philadelphia, USA, (2004) 4.
R Arduino card. Retrieved: July, 12, 2017. Available at: http://arduino.cc/.
E. M. Kutz. Standard Handbook of Biomedical Engineering and Design. McGraw-Hill Companies,Inc., First edition, New York,
USA, (2003) 447-448.
M.Rezki & al., De-noising a Signal’s ECG sensor using various Wavelets Transforms and other analyzing techniques. International Journal of Applied Engineering Research, (2013) 589-599.
K. J. Blinowska, J. Zygierewicz, Practical Biomedical Signal Analysis Using MATLAB. CRC Press, Taylor & Francis Group, LLC, First edition, New York, USA, (2011) 54.
J. Rafiee & al., Wavelet basis functions in biomedical signal processing. Expert Systems with Applications, vol.38 , (2011) 6190–6201.
M.Ayad , D.Chikouche , N.Boukezzoula N and M.Rezki, Search of a robust defect signature in gear systems across adaptive Morlet wavelet of vibration signals. IET Signal Processing, 8/9, (2014) 918 –926.