Lima, Manuel (2021) Mecanomiografía: desarrollo de un dispositivo para el monitoreo del límite de fatiga muscular / Mechanomiagraphy: development of device for muscle fatigue monitoring. Proyecto Integrador Ingeniería Nuclear, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
En esta tesis se desarrollo un dispositivo de adquisición de señal para mecanomiografía de bajo costo, replicable y portátil. El mismo consta de un acelerómetro, electrónica de acondicionamiento de señal y una placa de desarrollo en la que se adquieren las señales de dos canales en simultaneo. Estas se procesan digitalmente y son transmitidas de manera inalámbrica a una aplicación multiplataforma de control y comunicación también desarrollada en este trabajo. Esta aplicación permite visualizar las señales en tiempo real y almacenar capturas. Estas se procesan fuera de línea según una metodología también diseñada en este trabajo para determinar que parámetros de la señal se relacionan con la actividad muscular. Los parámetros se calcularon sobre la señal original, sobre bandas de frecuencia específicas y sobre la distribución espectral de potencia. Por ultimo, se realizaron experimentos para aplicar la metodología propuesta a las señales adquiridas por el dispositivo desarrollado. Se realizaron ejercicios isométricos de extensión de rodilla sobre un brazo de reacción fijo vinculado a una celda de carga. Se midió la señal de mecanomiografía sobre el recto femoral de 5 sujetos en ejercicios a carga máxima, a distintos porcentajes de la carga máxima, y hasta el fallo de la tarea solicitada; y se realizaron pruebas de impacto sobre el musculo para evaluar las propiedades mecánicas del mismo. Se procesaron 145 parámetros, de los cuales se encontraron 12 con alta correlación con la fuerza ejercida (coeficiente de correlación r de Pearson 0; 8). Con las pruebas de impacto se encontró que la frecuencia natural del musculo aumenta con la fuerza. En ejercicios al fallo de la tarea solicitada, se encontró que los 12 parámetros seleccionados mantienen el valor esperado con la correlación hallada, para la carga del ejercicio, durante al menos 10 segundos, y luego presentan desviaciones significativas, pero con sentidos variados entre sujetos. Los parámetros relacionados con la energía de la señal en la banda de 50 a 75 Hz presentaron una correlación superior al resto (r = 0; 9) y desviaciones con el tiempo en los ejercicios al fallo consistentes en todos los sujetos y cargas evaluadas.
Resumen en inglés
A low cost, replicable and portable signal acquisition device for mechanomiography was developed in this thesis. It comprises an accelerometer, its signal conditioning electronics and a development board that acquires the signal in two channels. The signal is digitally processed and wirelessly transmitted to a multi-platform control and communication App, which is also developed in this thesis. This App enables the realtime visualization of the signal and fiits storage for offine processing, using a methodology designed in this thesis to get correlated features with muscle activity. These features where computed over the original signal, some band ltered components and the power spectrum density. Finally, experiments where conducted to apply the developed methodology to the signal acquired by the device. These experiments consisted of isometric knee extension exercises against a fixed reaction arm linked to a load cell. The mechanomiogram signal was acquired from the rectus femoris muscle of 5 participants at maximum load, at intermediate loads, and to task failure. Additionally, bump tests where conducted to evaluate the muscle mechanical properties. 145 features where processed, of which only 12 showed a high correlation with force (Pearson's r correlation coefficient 0; 8). Muscle natural frequency was found to increase with higher force production. The 12 selected parameters presented a constant value during the first 10 seconds of a fatiguing exercise, but showed significant deviations from the expected value after this, with varying directions between subjects. From the selected features, those related with the signal energy at the 50-75 Hz band presented both the highest correlation (r = 0; 9) and the most consistent deviations sense, in task failure exercises among all subjects and loads.
| Tipo de objeto: | Tesis (Proyecto Integrador Ingeniería Nuclear) |
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| Palabras Clave: | Muscles; Músculos; [Mecanomiography; Mecanomiografía; Signal acquisition; Adquisión de señales; Muscle fatigue; Fatiga muscular; Muscle contraction; Contracción muscular; Time-frequency analysis; Análisis tiempo-frecuencia] |
| Referencias: | [1] Joseph, A. A., Merboldt, K. D., Voit, D., Dahm, J., Frahm, J. Real-time magnetic resonance imaging of deep venous flow during muscular exercise-preliminary experience. Cardiovascular Diagnosis and Therapy, 6 (6), 473-481, 2016. 1 [2] Zahak, M. Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis. Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges, oct 2012. 2 [3] Guyton. Textbook of Medical Physiology. 2006. vii, 4, 5, 7, 9, 12, 13 [4] Neuromuscular Junctions and Muscle Contractions | Anatomy and Physiology I. URL https://courses.lumenlearning.com/cuny-csi-ap-1/chapter/neuromuscular-junctions-and-muscle-contractions/. vii, 6 [5] Giganti, D., Yan, K., Badilla, C. L., Fernandez, J. M., Alegre-Cebollada, J. Disul de isomerization reactions in titin immunoglobulin domains enable a mode of protein elasticity. Nature Communications, 9 (1), 2018. vii, 6 [6] Krans, J. L. The sliding lament theory of muscle contraction, 2010. 8 [7] Spudich, J. A. The myosin swinging cross-bridge model. Nature Reviews Molecular Cell Biology, 2 (5), 387-392, 2001. URL https://doi.org/10.1038/35073086. vii, 8 [8] GitHub - iandanforth/pymuscle: A motor unit based model of skeletal muscle and fatigue. URL https://github.com/iandanforth/pymuscle. vii, 9 [9] Celichowski, J. Mechanisms underlying the regulation of motor unit contraction in the skeletal muscle, 2000. vii, 10, 12 [10] Mantilla, C. B., Sieck, G. C. Invited review: Mechanisms underlying motor unit plasticity in the respiratory system. Journal of Applied Physiology, 94 (3), 1230-1241, mar. 2003. URL https://doi.org/10.1152/japplphysiol.01120.2002. vii, 10 [11] Floeter, M. K. Structure and function of muscle bers and motor units. En: Disorders of Voluntary Muscle, pags. 1-19. Cambridge University Press, 2010. URL https://www.cambridge.org/core/books/disorders-of-voluntary-muscle/structure-and-function-of-muscle-fibers-and-motor-units/ D8DBDA5D8D7525311152A252C2CBDA36. vii, 11 [12] Radak, Z. Skeletal muscle, function, and muscle ber types. En: The Physiology of Physical Training, pags. 15-31. Elsevier, 2018. URL https://doi.org/10.1016/b978-0-12-815137-2.00002-4. 13, 26, 27 [13] Hughes, C. Skeletal Muscle Structure, Function, and Plasticity: The Physiological Basis of Rehabilitation, 2nd Edition. Medicine and Science in Sports and Exercise, 2003. vii, 14 [14] Pereira, A. F. Development of a Hill-Type Muscle Model With Fatigue for the Calculation of the Redundant Muscle Forces using Multibody Dynamics. pag. 138, 2009. vii, 15 [15] Romero, F., Alonso, F. J. A comparison among dierent Hill-type contraction dynamics formulations for muscle force estimation. Mechanical Sciences, 7 (1), 19-29, jan 2016. viii, 15, 16 [16] Vaz, A. V. Mechanism of Muscle Vibrations During Stimulated and Voluntary Isometric Contractions of Mammalian Skeletal Muscle. Doctor of philosophy, University of Calgary, 1996. viii, xi, 17, 18, 20, 21, 22, 27, 28, 71, 123 [17] Sleboda, D. A., Roberts, T. J. Incompressible fluid plays a mechanical role in the development of passive muscle tension. Biology Letters, 13 (1), 20160630, ene. 2017. URL https://doi.org/10.1098/rsbl.2016.0630. 18 [18] Hendrix, C. R., Housh, T. J., Zuniga, J. M., Camic, C. L., Mielke, M., Johnson, G. O., et al. A mechanomyographic frequency-based fatigue threshold test. Journal of Neuroscience Methods, 187 (1), 1-7, mar 2010. viii, 23, 25 [19] Silva, J., Chau, T. A mathematical model for source separation of MMG signals recorded with a coupled microphone-accelerometer sensor pair. IEEE Transactions on Biomedical Engineering, 52 (9), 1493-1501, sep 2005. viii, 23 [20] Fukuhara, S., Watanabe, S., Oka, H. Novel mechanomyogram/electromyogram hybrid transducer measurements re ect muscle strength during dynamic exercise| Pedaling of recumbent bicycle. Advanced Biomedical Engineering, 7, 47-54, 2018. viii, 24 [21] TMG Science for Body Evolution. URL https://www.tmg-bodyevolution.com/about-tmg/education/. viii, 24 [22] Orizio, C. Muscle sound : Bases for the introduction of a mechanomyographic signal in muscle studies. (February 1993), 1993. 24, 27, 29, 30 [23] Ibitoye, M. O., Hamzaid, N. A., Zuniga, J. M., Hasnan, N., Wahab, A. K. A. Mechanomyographic parameter extraction methods: An appraisal for clinical applications. Sensors (Switzerland), 14 (12), 22940-22970, dec 2014. 25 [24] Orizio, C., Gobbo, M., Diemont, B., Esposito, F., Veicsteinas, A. The surface mechanomyogram as a tool to describe the influence of fatigue on biceps brachii motor unit activation strategy. Historical basis and novel evidence. European Journal of Applied Physiology, 90 (3-4), 326-336, oct 2003. 25 [25] BIOPAC. Application Note 261 Precision and accuracy of the TSD250 ( BPS-II ) VMG transducer in the assessment of isometric absolute muscle eort. 250, 2010, 2010. 25 [26] Lozano-Garca, M., Estrada, L., Jane, R. Performance evaluation of xed sample entropy in myographic signals for inspiratory muscle activity estimation. Entropy, 21 (2), feb 2019. 25 [27] Lozano-Garca, M., Sarlabous, L., Moxham, J., Raerty, G. F., Torres, A., Jane, R., et al. Surface mechanomyography and electromyography provide non-invasive indices of inspiratory muscle force and activation in healthy subjects. Scientic Reports, 8 (1), 1-13, dec 2018. 25 [28] Lozano-Garcia, M., Sarlabous, L., Moxham, J., Raerty, G. F., Torres, A., Jolley, C. J., et al. Assessment of Inspiratory Muscle Activation using Surface Diaphragm Mechanomyography and Crural Diaphragm Electromyography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018-July, 3342-3345, 2018. 25 [29] Xi, X., Yang, C., Miran, S. M., Zhao, Y. B., Lin, S., Luo, Z. SEMG-MMG State- Space Model for the Continuous Estimation of Multijoint Angle. Complexity, 2020, 2020. 25 [30] Wilson, S., Eberle, H., Hayashi, Y., Madgwick, S. O., McGregor, A., Jing, X., et al. Formulation of a new gradient descent MARG orientation algorithm: Case study on robot teleoperation. Mechanical Systems and Signal Processing, 130, 183-200, 2019. 25 [31] BIOPAC. Using Vibromyography to obtain length-tension curves for a quadriceps muscle (vastus lateralis), 2010. 25 [32] BIOPAC. Application note 262 - application of vmg in sports medicine: Assessing quadriceps-hamstring activity following acl reconstruction, 2011. 25 [33] Xu, L., Zhang, Y., Chan, K., Qin, L. Modeling of muscle vibration during a twitch. En: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286). IEEE. URL https://doi.org/10.1109/iembs.1998.745111. xi, 25, 71 [34] Buchthal, F., Schmalbruch, H. Contraction times and bre types in intact human muscle. Acta Physiologica Scandinavica, 79 (4), 435-452, ago. 1970. URL https://doi.org/10.1111/j.1748-1716.1970.tb04744.x. 26, 29 [35] BIOPAC. Application Note 250 - Vibromyography for the assessment of voluntary muscle eort. viii, 27, 128 [36] Krueger, E., Scheeren, E. M., Nogueira-Neto, G. N., Button, V. L. D. S. N., Nohama, P. Advances and perspectives of mechanomyography. Revista Brasileira de Engenharia Biomédica, 30 (4), 384-401, 2014. 28, 29, 96, 97, 128 [37] Wakeling, J. M., Nigg, B. M. Modification of soft tissue vibrations in the leg by muscular activity. Journal of Applied Physiology, 90 (2), 412{420, 2001. 28, 29, 128 [38] Allan G. Piersol, T. L. P. Harris' Shock And Vibration Handbook. x, 29, 30, 58, 59, 60 [39] Beck, T. W., Housh, T. J., Johnson, G. O., Cramer, J. T., Weir, J. P., Coburn, J. W., et al. Does the frequency content of the surface mechanomyographic signal reffect motor unit ring rates? A brief review. Journal of Electromyography and Kinesiology, 17 (1), 1-13, feb 2007. 29 [40] Yoshitake, Y., Shinohara, M., Ue, H., Moritani, T. Characteristics of surface mechanomyogram are dependent on development of fusion of motor units in humans. Journal of Applied Physiology, 93 (5), 1744-1752, 2002. 29 [41] Bigland-Ritchie, B., Johansson, R., Lippold, O. C., Woods, J. J. Contractile speed and EMG changes during fatigue of sustained maximal voluntary contractions. Journal of Neurophysiology, 50 (1), 313-324, jul. 1983. URL https://doi.org/10.1152/jn.1983.50.1.313. 29 [42] Espresssif Systems. ESP32 Series: Datasheet, 2021. URL https://www.espressif.com/sites/default/files/documentation/esp32_ datasheet_en.pdf, v3.6. ix, 34, 35 [43] Analog Devices. Datasheet AD8226: Wide Supply Range, Rail-to-Rail Output Instrumentation Amplier, 2019. Rev. D. 42, 43 [44] Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., et al. Scipy 1.0: Fundamental algorithms for scientic computing in python. Nature Methods, 17, 261-272, 2020. 47, 64, 97 [45] Britch, D. enterprise Application Patterns using Xamarin.Forms. Microsoft Corporation, 2017. 49 [46] Freescale Semiconductor. Micromachined Accelerometer MMA7361L, 2008. URL https://www.nxp.com/docs/en/data-sheet/MMA7361L.pdf. 53 [47] Yf-602 digital multimeter. URL https://irelectronics.pe/wp-content/uploads/2020/08/FICHA-TECNICA-YF-602.pdf. 54 [48] Shannon, C. E. Communication in the presence of noise. Proceedings of the IEEE, 86 (2), 447-457, 1998. 56 [49] Blinowska, K. J., Zygierewicz, J. Practical Biomedical Signal Analysis. 2012. x, 57, 60, 66, 67, 68, 75, 76, 78 [50] Randall, R., Tordon, M. Data adquisition. En: Encyclopedia of Vibration, pags. 364-376. Elsevier, 2001. URL https://doi.org/10.1006/rwvb.2001.0142. 61 [51] ATMEL Corporation. AVR121: Enhancing ADC resolution by oversampling Microcontrollers. pags. 1-48, 2005. 61 [52] Ingle, V. K., Proakis, J. G. Digital signal processing using MATLAB. Cengage Learning, 2012. 62, 95 [53] Dziedziech, K., Staszewski, W. J., Uhl, T. Wavelet-based frequency response function: Comparative study of input excitation. Shock and Vibration, 2014, 1-11, 2014. URL https://doi.org/10.1155/2014/502762. xi, 67 [54] Valens, C. A Really Friendly Guide to Wavelets, 1999. URL http://agl.cs. unm.edu/$\sim$williams/cs530/arfgtw.pdf. xi, 68, 69 [55] Lee, G. R., Gommers, R., Wohlfahrt, K., Wasilewski, F., O'Leary, A., Nahrstaedt, H., et al. Pywavelets/pywt: Pywavelets 1.1.1, oct. 2019. URL https://doi.org/10.5281/zenodo.3510098. 69 [56] Barry, D. Vibrations and sounds from evoked muscle twitches. Electromyography and clinical neurophysiology, 32 (1-2), 35|40, 1992. URL http://europepmc.org/abstract/MED/1541245. xi, 71 [57] Donoho, D. L., Johnstone, M. I. Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika, 81 (3), 425-455, 1994. 71 [58] Johnstone, I. M., Silverman, B. W. Wavelet Threshold Estimators for Data with Correlated Noise. Royal Statistical Society, 59 (2), 319-351, 1997. 72 [59] Moca, V. V., B^arzan, H., Nagy-Dab^acan, A., Mures,an, R. C. Time-frequency super-resolution with superlets. Nature Communications, 12 (1), 1-18, dec 2021. xi, 75, 76 [60] Xi, X., Tang, M., Miran, S. M., Luo, Z. Evaluation of feature extraction and recognition for activity monitoring and fall detection based on wearable sEMG sensors. Sensors (Switzerland), 17 (6), 1-20, jun 2017. 77, 79 [61] Fara, S., Vikram, C. S., Gavriel, C., Faisal, A. A. Robust, ultra low-cost MMG system with brain-machine-interface applications. International IEEE/EMBS Con- ference on Neural Engineering, NER, pags. 723-726, 2013. 79 [62] Myers, A., Khasawneh, F. A. On the automatic parameter selection for permutation entropy. Chaos, 30 (3), 2020. xi, 80 [63] Zanin, M., Zunino, L., Rosso, O. A., Papo, D. Permutation entropy and its main biomedical and econophysics applications: A review. Entropy, 14 (8), 1553-1577, 2012. 80, 81 [64] Islam, M. A., Sundaraj, K., Ahmad, R. B., Ahamed, N. U. Mechanomyogram for Muscle Function Assessment: A Review. PLoS ONE, 8 (3), mar 2013. 81 [65] Burden, R. L., Faires, J. D. Numerical analysis. Brooks/Cole Pub. Co., 2011. 81,82 [66] Phinyomark, A., Thongpanja, S., Hu, H., Phukpattaranont, P., Limsakul, C. The Usefulness of Mean and Median Frequencies in Electromyography Analysis. Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges, 2012. 81 [67] Wolfram mathworld: Skewness. URL https://mathworld.wolfram.com/Skewness.html. 82 [68] Wolfram mathworld: Kurtosis. URL https://mathworld.wolfram.com/Kurtosis.html. 82 [69] Ponce-Flores, M., Frausto-Sols, J., Santamara-Bonl, G., Perez-Ortega, J., Gonzalez-Barbosa, J. J. Time series complexities and their relationship to forecasting performance. Entropy, 22 (1), 89, ene. 2020. URL https://doi.org/10.3390/e22010089. 82 [70] Weisstein, E. W. Interquartile range. URL https://mathworld.wolfram.com/InterquartileRange.html. 83 [71] Devore, J. L. Probability and statistics for engineering and the sciences. 7a edon. Belmont, CA: Thomson/Brooks/Cole, 2008. 83, 84 [72] Ioannidis, J. P. A. The proposal to lower p value thresholds to .005. JAMA, 319 (14), 1429, abr. 2018. URL https://doi.org/10.1001/jama.2018.1536. 84 [73] Kendall, M. G. A new measure of rank correlation. Biometrika, 30 (1-2), 81-93, jun. 1938. URL https://doi.org/10.1093/biomet/30.1-2.81. 84, 85 [74] Box, G. E. P., Cox, D. R. An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological), 26 (2), 211-252, 1964. URL http://www.jstor.org/stable/2984418. 85 [75] Banks, D. L., Fienberg, S. E. Statistics, multivariate. En: Encyclopedia of Physical Science and Technology, pags. 851-889. Elsevier, 2003. URL https://doi.org/10.1016/b0-12-227410-5/00731-6. 86 [76] Calculadora de bmi. URL https://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmi-m_sp.htm. 92 [77] Truong, C., Oudre, L., Vayatis, N. Selective review of oine change point detection methods. Signal Processing, 167, 107299, feb. 2020. URL https://doi.org/10.1016/j.sigpro.2019.107299. 111 [78] Capitulo 14 instalaciones eléctricas. URL https://www.enre.gov.ar/web/bibliotd.nsf/58d19f48e1cdebd503256759004e862f/fb674409a000bfa1032569a6006f264b?OpenDocument. 126 |
| Materias: | Ingeniería mecánica > Bioingeniería |
| Divisiones: | Gcia. de área de Energía Nuclear > Gcia. de Ingeniería Nuclear > Vibraciones |
| Código ID: | 1016 |
| Depositado Por: | Tamara Cárcamo |
| Depositado En: | 09 May 2022 13:44 |
| Última Modificación: | 09 May 2022 13:44 |
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