Wavelet analysis of the EMG signal to assess muscle fatigue in the lower extremities during symmetric movement on a rowing ergometer

• Autorzy: Daniel Natalia , Małachowski Jerzy

Identyfikatory

DOI

10.37190/ABB-02282-2023-01

• Języki publikacji: EN

Abstrakty

EN

The aim of this publication was to propose a method to determine changes in fatigue in selected muscle groups of the lower extremity during dynamic and cyclical motion performed on a rowing ergometer. The study aimed to use the discrete wavelet transform (DWT) to analyze electromyographic signals (EMG) recorded during diagnostic assessment of muscle and peripheral nerve electrical activity (electroneurography) using an electromyography device (EMG). Methods: The analysis involved implementing calculations such as mean frequency (MNF) and median frequency (MDF) using the reconstructed EMG signal through DWT. The study examined the efficacy of DWT analysis in assessing muscle fatigue after physical exertion. Results: The study obtained a negative regression coefficient for DWT analysis in all muscles except for the right gastrocnemius (GAS). The results suggest that DWT analysis can be an effective tool for evaluating muscle fatigue after physical exertion. Conclusions: The use of DWT in the analysis of EMG signals during rowing ergometer exercises has shown promising results in assessing muscle fatigue. However, additional investigations are necessary to confirm and expand these findings. This publication addresses the literature gap on the determination of muscle fatigue considering motion analysis on a rowing ergometer using the discrete wavelet transform. Previous studies have extensively compared and analyzed methods such as the Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet transform (WT) for muscle fatigue analysis. However, no previous work has specifically examined the assessment of muscle fatigue by incorporating DWT analysis with motion analysis on a rowing ergometer.

Słowa kluczowe

EN

DWT; fatigue; EMG; MNF; MDF; rowing ergometers

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Acta of Bioengineering and Biomechanics
• Rocznik: 2023
• Tom: Vol. 25, no. 2
• Strony: 15--27
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Daniel Natalia

• Military University of Technology, Faculty of Mechatronics, Armament and Aviation, Warsaw, Poland

autor

Małachowski Jerzy

• Military University of Technology, Faculty of Mechanical Engineering, Warsaw, Poland.

Bibliografia

• [1] AMENT W., VERKERKE G.J., Exercise and fatigue, Sports Medicine, 2009, 39, 5, 389–422, DOI: 10.2165/00007256- 200939050-00005.
• [2] BASTIAENSEN Y., SCHAEPS T., BAEYENS J.P., Analyzing an sEMG signal using wavelets, 4th European Conference of the International Federation for Medical and Biological Engineering, 2009, 156–159, DOI: 10.1007/978-3-540-89208-3_39.
• [3] BECK T.W., HOUSH T.J., JOHNSON G.O., WEIR J.P., CRAMER J.T., COBURN J.W., MALEK M.H., Comparison of Fourier and wavelet transform procedures for examining the mechanomyographic and electromyographic frequency domain responses during fatiguing isokinetic muscle actions of the biceps brachii, Journal of Electromyography and Kinesiology, 2005, 15, 2, 190–199, DOI: 10.1016/j.jelekin.2004.08.007.
• [4] BOYER M., BOUYER L., ROY J.S., CAMPEAU-LECOURS A., A real-time algorithm to estimate shoulder muscle fatigue based on surface EMG signal for static and dynamic upper limb tasks, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2021, 100–106, DOI: 10.1109/ EMBC46164.2021.9630702.
• [5] CAMATA T. V., DANTAS J.L., ABRÃO T., BRUNETTO M.A.O.C., MORAES A.C., ALTIMARI L.R., Fourier and wavelet spectral analysis of EMG signals in supramaximal constant load dynamic exercise, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10, 2010, 1364–1367, DOI: 10.1109/IEMBS.2010.5626743.
• [6] CANAL M.R., Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals, J. Med. Syst., 2010, 34, 1, 91–94, DOI: 10.1007/s10916-008-9219-8.
• [7] CHOWDHURY R., REAZ M.B.I., ISLAM M.T., Wavelet transform to recognize muscle fatigue, Third Asian Himalayas International Conference on Internet, 2012, 1–5, DOI: 10.1109/ AHICI.2012.6408445.
• [8] CHOWDHURY S.K., NIMBARTE A.D., JARIDI M., CREESE R.C., Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles, J. Electromyogr. Kinesiol., 2013, 23, 5, 995–1003, DOI: 10.1016/j.jelekin.2013.05.001.
• [9] CHOWDHURY S.K., NIMBARTE A.D., Comparison of Fourier and wavelet analysis for fatigue assessment during repetitive dynamic exertion, J. Electromyogr. Kinesiol., 2015, 25, 2, 205–213, DOI: 10.1016/j.jelekin.2014.11.005.
• [10] CIFREK M., MEDVED V., TONKOVIĆ S., OSTOJIĆ S., Surface EMG based muscle fatigue evaluation in biomechanics, Clinical Biomechanics, 2009, 24, 4, 327–340, DOI: 10.1016/ j.clinbiomech.2009.01.010.
• [11] DANIEL N., SYBILSKI K., KACZMAREK W., SIEMIASZKO D., MAŁACHOWSKI J., Relationship between EMG and fNIRS during dynamic movements, Sensors, 2023, 23, 11, DOI: 10.3390/ s23115004.
• [12] DANTAS J.L., CAMATA T. V., BRUNETTO M.A.O.C., MORAES A.C., ABRÃO T., ALTIMARI L.R., Fourier and Wavelet spectral analysis of EMG signals in isometric and dynamic maximal effort exercise, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC ’10, 2010, 5979–5982, DOI: 10.1109/IEMBS.2010.5627579.
• [13] DEJNEKA A., MAŁACHOWSKI J., MAZURKIEWICZ L., Identification of muscle movements and activity by experimental methods for selected cases – stage#1, Acta Bioeng. Biomech., 2022, https://doi.org/10.37190/ABB-02103-2022-02.
• [14] DEJNEKA A., MAŁACHOWSKI J., MAZURKIEWICZ Ł., Identification of muscle movements and activity by experimental methods for selected cases – stage#2, Acta Bioeng. Biomech., 2022, DOI: 10.37190/ABB-02104-2022-02.
• [15] IDREES B.M., FAROOQ O., Estimation of muscle fatigue using wavelet decomposition, Fifth International Conference on Digital Information Processing and Communications (ICDIPC), 2015, 267–271, DOI: 10.1109/ICDIPC.2015.7323040.
• [16] JABŁOŃSKA M., MĄCZYŃSKI J., FRYZOWICZ A., OGURKOWSKA M.B., Electromyographic assessment of muscle fatigue after the Biering–Sorensen test in subjects with low back pain who underwent the McKenzie treatment, Acta Bioeng. Biomech., 2021, 23, 3, 87–96, DOI: 10.37190/ABB01823-2021-03.
• [17] JURECKA A., ŻMIJEWSKA K., PAPLACZYK M., GĄDEK A., The effect of cervical spine rotation on electromyographic activity of the trapezius and serratus anterior during selected shoulder complex movements, Acta Bioeng. Biomech., 2021, 24, 1, DOI: 10.37190/ABB-01988-2021-02.
• [18] KARLSSON S., GERDLE B., Mean frequency and signal amplitude of the surface EMG of the quadriceps muscles increase with increasing torque – a study using the continuous wavelet transform, Journal of Electromyography and Kinesiology, 2001, 11, 131–140.
• [19] KAUR G., ARORA A.S., JAIN V.K., Comparison of the techniques used for segmentation of EMG signals, WSEAS, 2009.
• [20] KOPEĆ K., BEREZA P., SOBOTA G., HAJDUK G., KUSZ D., The electromyographic activity characteristics of the gluteus medius muscle before and after total hip arthroplasty, Acta Bioeng. Biomech., 2021, 23, 1, 187–195, DOI: 10.37190/ ABB-01753-2020-02.
• [21] KUMAR D.K., PAH N.D., BRADLEY A., Wavelet analysis of surface electromyography to determine muscle fatigue, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003, 11, 4, 400–406, DOI: 10.1109/TNSRE.2003.819901.
• [22] KUNISZYK-JÓŹKOWIAK W., JASZCZUK J., CZAPLICKI A., Changes in electromyographic signals and skin temperature during standardised effort in volleyball players, Acta Bioeng. Biomech., 2018, 20, 3, 115–122, DOI: 10.5277/ABB-01198-2018-02.
• [23] KUTHE C.D., UDDANWADIKER R. V., RAMTEKE A.A., Surface electromyography based method for computing muscle strength and fatigue of biceps brachii muscle and its clinical implementation, Informatics in Medicine Unlocked, 2018, 12, 34–43, DOI: 10.1016/j.imu.2018.06.004.
• [24] MACISAAC D., PARKER P.A., SCOTT R.N., The short-time Fourier transform and muscle fatigue assessment in dynamic contractions, Journal of Electromyography and Kinesiology, 2001, 11, 439–449, DOI: 10.1016/S1050-6411(01)00021-9.
• [25] MATHWORKS, Continuous and Discrete Wavelet Transforms, https://www.mathworks.com/help/wavelet/gs/continuous-and -discrete-wavelet-transforms.html, last accessed 2023/06/27.
• [26] PHINYOMARK A., LIMSAKUL C., PHUKPATTARANONT P., Application of wavelet analysis in EMG feature extraction for pattern classification, Measurement Science Review, 2011, 11, 2, 45–52, DOI: 10.2478/v10048-011-0009-y.
• [27] 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, DOI: 10.5772/50639.
• [28] REAZ M.B.I., HUSSAIN M., MOHD-YASIN F., EMG analysis using wavelet functions to determine muscle contraction, HEALTHCOM 2006 8th International Conference on e-Health Networking, Applications and Services, New Delhi, India, 2006, 132–134, DOI: 10.1109/HEALTH.2006.246433.
• [29] SAHIN U., SAHIN F., Pattern recognition with surface EMG signal based wavelet transformation, Conf. Proc. IEEE Int. Conf. Syst. Man. Cybern., 2012, 295–300, DOI: 10.1109/ ICSMC.2012.6377717.
• [30] SCHWENSOW D., HOHMUTH R., MALBERG H., SCHMIDT M., Investigation of muscle fatigue during on-water rowing using surface EMG, 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022, 3623–3627, DOI: 10.1109/EMBC48229.2022.9872010.
• [31] SEDLÁK J., ŠPULÁK D., ČMEJLA R., BAČÁKOVÁ R., CHRÁSTKOVÁ M., KRAČMAR B., Segmentation of surface EMG signals, International Conference on Applied Electronics, Pilsen, Czech Republic, 2013, 1–4.
• [32] SHIAO Y., HOANG T., Exercise condition sensing in smart leg extension machine, Sensors, 2022, 22, 17, DOI: 10.3390/ s22176336.
• [33] SPARTO P.J., PARNIANPOUR M., BARRIA E.A., JAGADEESH J.M., Wavelet and short-time fourier transform analysis of electromyography for detection of back muscle fatigue, IEEE Transactions on Rehabilitation Engineering, 2000, 8, 3, 433–436, DOI: 10.1109/86.867887.
• [34] THONGPANJA S., PHINYOMARK A., PHUKPATTARANONT P., LIMSAKUL C., A feasibility study of fatigue and muscle contraction indices based on EMG time-dependent spectral analysis, Procedia Eng., 2012, 32, 239–245, DOI: 10.1016/ j.proeng.2012.01.1263.
• [35] THONGPANJA S., PHINYOMARK A., PHUKPATTARANONT P., LIMSAKUL C., Mean and median frequency of EMG signal to determine muscle force based on time dependent power spectrum, Elektronika ir Elektrotechnika, 2013, 19, 3, 51–56, DOI: 10.5755/j01.eee.19.3.3697.
• [36] VANNOZZI G., CONFORTO S., D’ALESSIO T., Automatic detection of surface EMG activation timing using a wavelet transform-based method, J. Electromyogr. Kinesiol., 2010, 20, 4, 767–772, DOI: 10.1016/j.jelekin.2010.02.007.
• [37] VIEIRA T.M., CERONE G.L., STOCCHI C., LALLI M., ANDREWS B., GAZZONI M., Timing and modulation of activity in the lower limb muscles during indoor rowing: What are the key muscles to target in FES-rowing protocols?, Sensors, 2020, 20, 6, DOI: 10.3390/s20061666.
• [38] WAN J.J., QIN Z., WANG P.Y., SUN Y., LIU X., Muscle fatigue: general understanding and treatment, Exp. Mol. Med., 2017, 49, e384, DOI: 10.1038/emm.2017.194.
• [39] WILLIAMS N., The Borg rating of perceived exertion (RPE) scale, Occupational Medicine, 2017, 67, 5, 404–405, DOI: 10.1093/occmed/kqx063.
• [40] WODARSKI P., JURKOJĆ J., GZIK M., Wavelet decomposition in analysis of impact of virtual reality head mounted display systems on postural stability, Sensors, 2020, 20, 24, 1–12, DOI: 10.3390/s20247138.
• [41] XIAO S., LEUNG S.C., Muscle fatigue monitoring using wavelet decomposition of surface EMG, Biomed. Sci. Instrum., 1997, 34, 147–152.
• [42] YOCHUM M., BAKIR T., BINCZAK S., LEPERS R., Electromyostimulation and EMG real time device with muscle fatigue estimation, XXVII Conférence on Design of Circuit and Integrated Systems, France, 2012, 1–4, https://hal.science/hal00764939, (Accessed: 2023.07.27).
• [43] ZHANG X., WANG Y., HAN R.P.S., Wavelet transform theory and its application in EMG signal processing, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, China, 2010, 5, 2234–2238, DOI: 10.1109/ FSKD.2010.5569532.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).