Eddy Krueger, Eduardo M. Scheeren, Carla Daniele Pacheco Rinaldin, André E. Lazzaretti, Eduardo Borba Neves, Guilherme Nunes Nogueira-Neto, Percy Nohama

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Surface mechanography (MMG) is a non-invasive technique that captures signs of low-frequency vibrations of skeletal muscles through the skin. However, subcutaneous structures may interfere with the acquisition of MMG signals. The objective of this study was to verify the influence of skinfold thickness (ST) on the MMG wavelet-based signal in the rectus femoris muscle during maximal voluntary contraction in two groups of individuals: group I (n = 10, ST <10 mm ) and group II (n = 10, ST equal to or> 20 mm). Negative correlation was observed between the 19 Hz, 28 Hz and 39 Hz frequency bands with ST. There was a statistical difference in almost all frequency bands, especially in the X and Y axes. All MMG axes in group II presented higher magnitudes in frequency bands 2 and 6 Hz (like low-pass filter). Thus, these results can be applied to calibrate MMG responses as biofeedback systems.


Skinfold thickness, Wavelet, Mechanomyography

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Yu, N.Y. and S.H. Chang, The Characterization of Contractile and Myoelectric Activities in Paralyzed Tibialis Anterior Post Electrically Elicited Muscle Fatigue. Artificial Organs, 2010. 34(4): p. E117-E121.

Krueger, E., et al. Mechanomyography analysis with 0.2 s and 1.0 s time delay after onset of contraction. in BIOSTEC 2010: 3rd International Joint Conference on Biomedical Engineering Systems and Technologies. 2010. Valence.

Krueger, E., et al., Correlation between spectral and temporal mechanomyography features during functional electrical stimulation. Research on Biomedical Engineering, 2016. 32(1): p. 85-91.

Popovic, M.R. and T.A. Thrasher, Neuroprostheses, in Encyclopedia of Biomaterials and Biomedical Engineering, G.L. Bowlin and G. Wnek, Editors. 2004, Informa Healthcare: New York. p. 1056–65.

Vedsted, P., et al., Biofeedback effectiveness to reduce upper limb muscle activity during computer work is muscle specific and time pressure dependent. Journal of Electromyography & Kinesiology, 2011. 21(1): p. 49-58.

Trevino, M.A. and T.J. Herda, Mechanomyographic mean power frequency during an isometric trapezoid muscle action at multiple contraction intensities. Physiological measurement, 2015. 36(7): p. 1383.

Alves, N. and T. Chau, Automatic detection of muscle activity from mechanomyogram signals: a comparison of amplitude and wavelet-based methods. Physiological Measurement, 2010. 31: p. 461-76.

Yoshitake, Y., et al., Characteristics of surface mechanomyogram are dependent on development of fusion of motor units in humans. Journal of Applied Physiology, 2002. 93(5): p. 1744-1752.

Yoshitake, Y. and T. Moritani, The muscle sound properties of different muscle fiber types during voluntary and electrically induced contractions. Journal of Electromyography and Kinesiology, 1999. 9(3): p. 209-217.

Krueger, E., et al. Influence of skinfold thickness in mechanomyography features. in World Congress on Medical Physics and Biomedical Engineering. 2012. Beijing, China: IFMBE.

Herda, T.J., et al., A noninvasive, log-transform method for fiber type discrimination using mechanomyography. Journal of Electromyography and Kinesiology, 2010. 20(5): p. 787-794.

Chan, Y.T., Wavelet basics. 1995, Boston: Kluwer Academic. 123.

Chui, C.K., An introduction to wavelets. 1992, San Diego: Academic Press. 266.

Peñailillo, L., R. Silvestre, and K. Nosaka, Changes in surface EMG assessed by discrete wavelet transform during maximal isometric voluntary contractions following supramaximal cycling. European Journal of Applied Physiology, 2012. 113(4): p. 895-904.

von Tscharner, V., Intensity analysis in time-frequency space of surface myoelectric signals by wavelets of specified resolution. Journal of Electromyography and Kinesiology, 2000. 10(6): p. 433-445.

Beck, T.W., et al., Time/frequency events of surface mechanomyographic signals resolved by nonlinearly scaled wavelets. Biomedical Signal Processing and Control, 2008. 3(3): p. 255-266.

Matsunaga, T., Y. Shimada, and K. Sato, Muscle fatigue from intermittent stimulation with low and high frequency electrical pulses. Archives of Physical Medicine and Rehabilitation, 1999. 80(1): p. 48-53.

Baptista, R.R., et al., Low-frequency fatigue at maximal and submaximal muscle contractions. Brazilian Journal of Medical and Biological Research, 2009. 42: p. 380-385.

Youn, W. and J. Kim, Feasibility of using an artificial neural network model to estimate the elbow flexion force from mechanomyography. Journal of Neuroscience Methods, 2011. 194: p. 386-93.

Uchiyama, T. and E. Hashimoto, System identification of the mechanomyogram from single motor units during voluntary isometric contraction. Medical & Biological Engineering & Computing, 2011. 49(9): p. 1035-43.

Stock, M.S., et al., Linearity and Reliability of the Mechanomyographic Amplitude Versus Concentric Dynamic Torque Relationships for the Superficial Quadriceps Femoris Muscles. Muscle & Nerve, 2009. 41: p. 324-49.

Beck, T.W., et al., A wavelet-based analysis of surface mechanomyographic signals from the quadriceps femoris. Muscle & Nerve, 2009. 39: p. 355-363.

Malek, M.H., et al., Comparison of mechanomyographic sensors during incremental cycle ergometry for the quadriceps femoris. Muscle & Nerve, 2010. 42(3): p. 394-400.

Stock, M.S., et al., Linearity and reliability of the mechanomyographic amplitude versus dynamic constant external resistance relationships for the biceps brachii. Physiological Measurement, 2010. 31: p. 1487-98.

Zuniga, J.M., et al., A Mechanomyographic Fatigue Threshold Test for Cycling. International Journal Sports Medicine, 2010. 31(09): p. 636-643.

Armstrong, W.J., et al., Reliability of mechanomyography and triaxial accelerometry in the assessment of balance. Journal of Electromyography and Kinesiology, 2010. 20: p. 726-31.

Herda, T.J., et al., Reliability of mechanomyographic amplitude and mean power frequency during isometric step and ramp muscle actions. Journal of Neuroscience Methods, 2008. 171(1): p. 104-109.

Søgaard, K., et al., Changed activation, oxygenation, and pain response of chronically painful muscles to repetitive work after training interventions: a randomized controlled trial. European Journal of Applied Physiology, 2011. 112(1): p. 173-181.

Jaskólska, A., et al., The effect of skinfold on frequency of human muscle mechanomyogram. Journal of Electromyography and Kinesiology, 2004. 14(2): p. 217-225.

Cooper, M.A., et al., Relationships between skinfold thickness and electromyographic and mechanomyographic amplitude recorded during voluntary and non-voluntary muscle actions. Journal of Electromyography and Kinesiology, 2014. 24(2): p. 207-213.

Mealing, D., G. Long, and P. McCarthy, Vibromyographic recording from human muscles with known fibre composition differences. British journal of sports medicine, 1996. 30(1): p. 27-31.

Cescon, C., et al., Effect of accelerometer location on mechanomyogram variables during voluntary, constant-force contractions in three human muscles. Medical and Biological Engineering and Computing, 2004. 42(1): p. 121-127.

WOLLASTON, W.H., On the duration of muscle action. Philos. Trans. R. Soe, 1810: p. 1-5.

Maggi, L.E., et al., Software didático para modelagem do padrão de aquecimento dos tecidos irradiados por ultra-som fisioterapêutico. Revista brasileira de Fisioterapia, 2008. 12(3): p. 204-214.

Polato, D., M.C. Carvalho, and M.A.C. Garcia, Efeitos de dois parâmetros antropométricos no comportamento do sinal mecanomiográfico em testes de força muscular. Revista Brasilera de Medicina no Esporte, 2008. 14(3): p. 221-226.

Zuniga, J.M., et al., The effects of skinfold thicknesses and innervation zone on the mechanomyographic signal during cycle ergometry. Journal of Electromyography and Kinesiology, 2011. 25(5): p. 789-94.

Frangioni, J.V., et al., The mechanism of low-frequency sound production in muscle. Biophysical journal, 1987. 51.5 p. 775.



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