I'm analyzing fatigue in brachii muscles during cyclic dynamic contractions. from literature, i came to know that RMS and median mean frequency are not proper parameters to estimate muscle fatigue during dynamic contractions. some researchers suggested RQA time analysis, iEMG, instantaneous mean and median frequency, IIS, wavelet transform etc etc.
are the instantaneous mean, instantaneous median frequency, integrated EMG for RMS are correct options???
Dear Ibraheem,
Thank you for reply. how to reach statistical measure from raw data?
standard deviation of what? the amplitude points..... i have data of around 2 minutes (120 sec) for 1 exercise, sampled at 1kHz, for one subject. i just cannot get your point of standard deviation
Dear Jawad,
Maybe the following papers will help you:
Al-Mulla MR, Sepulveda F, Colley M. A review of non-invasive techniques to detect and predict localised muscle fatigue. Sensors (Basel) 2011;11(4):3545-94. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231314/pdf/sensors-11-03545.pdf
Shair EF, Ahmad SA, Marhaban MH, Mohd Tamrin SB, Abdullah AR. EMG Processing Based Measures of Fatigue Assessment during Manual Lifting. Biomed Res Int 2017;2017:3937254. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5337807/pdf/BMRI2017-3937254.pdf
Ali A, Sundaraj K, Badlishah Ahmad R, Ahamed NU, Islam A, Sundaraj S. Muscle Fatigue in the Three Heads of the Triceps Brachii During a Controlled Forceful Hand Grip Task with Full Elbow Extension Using Surface Electromyography. J Hum Kinet 2015;46:69-76. https://www.degruyter.com/downloadpdf/j/hukin.2015.46.issue-1/hukin-2015-0035/hukin-2015-0035.pdf
Nurul Asyikin Kamaruddin, Puspa Inayat Khalid, Ahmad Zuri Shaameri. The Use of Surface Electromyography in Muscle Fatigue Assessments - A Review. Jurnal Teknologi (Sciences & Engineering) 2015;74(6):1-5. http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/download/4676/3261
González-Izal M, Rodríguez-Carreño I, Malanda A, Mallor-Giménez F, Navarro-Amézqueta I, Gorostiaga EM, Izquierdo M. sEMG wavelet-based indices predicts muscle power loss during dynamic contractions. J Electromyogr Kinesiol 2010;20(6):1097-106. http://azadproject.ir/wp-content/uploads/2013/12/2011-O59-sEMG-wavelet-based-indices-predicts-muscle-power-loss-during-dynamic-contractions.pdf
Best wishes from Germany,
Martin
Following parameters have been used in literature for peripheral fatigue analysis in skeletal muscles.
ARV Average Rectified Value (isometric, dynamic)
DSP Dimitrov Spectral Parameters
iEMG integrated EMG (dynamic)
MaAV Maximal Amplitude Value (dynamic)*
MAV Mean Amplitude Value (average EMG value) (isometric, dynamic)
MDF Median Frequency ( isometric, dynamic)
MDFFFT Median Frequency using FFT (delicate movements, dynamic, isometric)
MDFSTFT Median Frequency using FFT (dynamic, isometric)
MFA Multi-fractal Analysis (dynamic)
MPF Mean Power Frequency (delicate movements, dynamic, isometric)
MPFDFT Mean Power Frequency using DFT (isometric)
MPFin Instantaneous Mean Power Frequency (dynamic)
RMS Root Mean Square (delicate movements, dynamic, isometric)
RQA Reoccurrence Quantification Analysis (dynamic)
WIG Wavelet Index by Gonzalez (dynamic)
*Luttmann, A. and Jager, M., “Reduction of Muscular Strain by Work Design: Electromyographical Field Studies in a Weaving Mill,” in: Advances in Industrial Ergonomics and Safety IV, Kumar, S., (Ed.), Taylor & Francis, pp. 553-560, 1992.