下背痛的罹患率逐年攀升，而長期痠痛的累積是導致下背痛主因之一，若能於下背肌群因過度疲勞痠痛前，警示使用者適當地放鬆，則可降低下背痛的風險。本研究針對下背痛預防治療醫學，開發監測下背疲勞程度之肌電訊號穿戴式裝置，於有網路覆蓋的環境，透過Wi-Fi連接個人行動裝置進行肌電訊號的採集、處理與分析，並即時顯示下背肌群疲勞程度，以達到事前預防的效果。
穿戴裝置軟體可過濾肌電訊號雜訊，於類比數位轉換後即時進行數位濾波、白化與平滑化處理，並以短時距傅立葉變換觀察頻域隨時間的變化。以肌電訊號方均根及中位數頻率作為疲勞判斷指標，並綜合此兩指標估計個人之下背肌群疲勞程度加以呈現。
系統驗證以任意函式產生器與示波器為標準，檢驗系統硬體接收訊號的可行性，以MATLAB驗證系統軟體輸出的合理性。臨床以十名受試者進行穩定性及重複性實驗，處理後肌電訊號間平均相關係數介於0.78至0.8，中位數頻率與方均根則皆無顯著差異。再以六名受試者進行下背肌群疲勞測試，於趨向疲勞過程，中位數頻率線性迴歸擬合皆有下降、方均根則多有上升，於下背肌群疲勞程度皆有明顯上升趨勢。

More and more people have suffered from low back pain in recent years. Low back muscles fatigue in long term is one of the major causes of low back pain. The incidence of low back pain can be reduced if people are notified when their low back muscles are about to fatigue, allowing the low back muscles to be relaxed. For preventive medical applications, we developed a wearable surface electromyography (EMG) system for monitoring lower back fatigue. The wearable device is worn on the upper back and used to receive, process and analyze EMG signal. Users can connect the wearable device to a mobile via Wi-Fi in the workplace with internet, and the mobile application can graphically present the level of lower back muscles fatigue in real time.
The wearable device is used to filter the noise of EMG signal and whitening, smoothing EMG signal in real time after analog-to-digital conversion. We observe variety of the EMG signal in frequency domain over time with short-time Fourier transform (STFT). The root mean square and median frequency of the EMG signal are extracted as fatigue judgment indicators to estimate the level of lower back muscles fatigue.
We used the arbitrary function generator and oscilloscope as standard to verify the feasibility of the system hardware receiving signals and used MATLAB to verify the correctness of the system software output. In the stability and reproducibility experiments, we recruited ten healthy subjects. The average correlation coefficient between processed EMG signals ranged from 0.78 to 0.8, and there is no significant difference in the median frequency and root mean square. Six healthy subjects were recruited to test the fatigue of the lower back muscles. In linear regression analysis, when the low back muscles of subjects tended to be fatigued, the median frequency all decreased, and the root mean square increased. As a result, the level of lower back muscles fatigue all had a significant upward trend.

[1] Andersson, G. B., "Epidemiological features of chronic low-back pain," The lancet, Vol.354, No.9178, pp.581-585, 1999.
[2] Roy, S. H., De Luca, C. J., and Casavant, D. A., "Lumbar muscle fatigue and chronic lower back pain," Spine, Vol.14, No.9, pp.992-1001, 1989.
[3] Stulen, F. B. and De Luca, C. J., "Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity," IEEE Transactions on biomedical engineering, No.7, pp.515-523, 1981.
[4] 衛生福利部中央健康保險署, "2018年台灣全民健康保險就醫疾病資料," <https://www.nhi.gov.tw/Content_List.aspx?n=D529CAC4D8F8E77B&topn=23C660CAACAA159D>, accessed on 6/25/2021
[5] Bener, A., Dafeeah, E. E., and Alnaqbi, K., "Prevalence and correlates of low back pain in primary care: what are the contributing factors in a rapidly developing country," Asian spine journal, Vol.8, No.3, p.227, 2014.
[6] Descarreaux, M., Lafond, D., Jeffrey-Gauthier, R., Centomo, H., and Cantin, V., "Changes in the flexion relaxation response induced by lumbar muscle fatigue," BMC musculoskeletal disorders, Vol.9, No.1, pp.1-9, 2008.
[7] Basmajian, J. V., DeLuca, C., Muscles Alive: Their Functions Revealed by Electromyography, Baltimore: Williams and Wilkins, 1983.
[8] Reaz, M. B. I., Hussain, M. S., and Mohd-Yasin, F., "Techniques of EMG signal analysis: detection, processing, classification and applications," Biological procedures online, Vol.8, No.1, pp.11-35, 2006.
[9] Konrad, P., "The abc of emg: a practical introduction to kinesiological electromyography," A practical introduction to kinesiological electromyography, Vol.1, No.2005, pp.30-5, 2005.
[10] Whalen, W. J., "Apparent exception to the" all or none" law in cardiac muscle," Science, Vol.127, No.3296, pp.468-469, 1958.
[11] Enoka, R. M., Neuromechanical basis of kinesiology, ERIC, 1988.
[12] Winter, D. A., Biomechanics and motor control of human movement, John Wiley & Sons, 2009.
[13] Kumar, S. and Mital, A., Electromyography in ergonomics, CRC Press, 1996.
[14] Kleissen, R., Buurke, J., Harlaar, J., and Zilvold, G., "Electromyography in the biomechanical analysis of human movement and its clinical application," Gait & posture, Vol.8, No.2, pp.143-158, 1998.
[15] Cram, J. R., Kasman, G. S., and Holtz, J., Introduction to surface electromyography, Aspen publishers, 1998.
[16] Shahid, S., "Higher order statistics techniques applied to EMG signal analysis and characterization," University of Limerick PhD Thesis, 2004.
[17] Cram, J. R., "The history of surface electromyography," Applied psychophysiology and biofeedback, Vol.28, No.2, pp.81-91, 2003.
[18] Hardyck, C. D., Petrinovich, L. F., and Ellsworth, D. W., "Feedback of speech muscle activity during silent reading: Rapid extinction," Science, Vol.154, No.3755, pp.1467-1468, 1966.
[19] Nikias, C. L. and Raghuveer, M. R., "Bispectrum estimation: A digital signal processing framework," Proceedings of the IEEE, Vol.75, No.7, pp.869-891, 1987.
[20] Wolf, S. and Basmajian, J., "Assessment of paraspinal electromyographic activity in normal subjects and in chronic back pain patients using a muscle biofeedback device," International series on biomechanics VIB, pp.319-324, 1978.
[21] Wolf, S. L., Nacht, M., and Kelly, J. L., "EMG feedback training during dynamic movement for low back pain patients," Behavior Therapy, Vol.13, No.4, pp.395-406, 1982.
[22] Ahern, D. K., Follick, M. J., Council, J. R., Laser-Wolston, N., and Litchman, H., "Comparison of lumbar paravertebral EMG patterns in chronic low back pain patients and non-patient controls," Pain, Vol.34, No.2, pp.153-160, 1988.
[23] Hermens, H. J., Freriks, B., Merletti, R., Stegeman, D., Blok, J., Rau, G., Disselhorst-Klug, C., and Hägg, G., "European recommendations for surface electromyography," Roessingh research and development, Vol.8, No.2, pp.13-54, 1999.
[24] Hermens, H. and Freriks, B., The state of the art on sensors and sensor placement procedures for surface electromyography: a proposal for sensor placement procedures, Enschede: Roessingh Research and Development, 1997.
[25] Merletti, R. and Hermens, H., "Introduction to the special issue on the SENIAM European Concerted Action," Journal of electromyography and kinesiology: official journal of the International Society of Electrophysiological Kinesiology, Vol.10, No.5, pp.283-286, 2000.
[26] Geisser, M. E., Ranavaya, M., Haig, A. J., Roth, R. S., Zucker, R., Ambroz, C., and Caruso, M., "A meta-analytic review of surface electromyography among persons with low back pain and normal, healthy controls," The journal of pain, Vol.6, No.11, pp.711-726, 2005.
[27] De Luca, C. J., "The use of surface electromyography in biomechanics," Journal of applied biomechanics, Vol.13, No.2, pp.135-163, 1997.
[28] Roy, S. H., De Luca, G., Cheng, M. S., Johansson, A., Gilmore, L. D., and De Luca, C. J., "Electro-mechanical stability of surface EMG sensors," Medical & biological engineering & computing, Vol.45, No.5, pp.447-457, 2007.
[29] Nordander, C., Willner, J., Hansson, G.-Å., Larsson, B., Unge, J., Granquist, L., and Skerfving, S., "Influence of the subcutaneous fat layer, as measured by ultrasound, skinfold calipers and BMI, on the EMG amplitude," European journal of applied physiology, Vol.89, No.6, pp.514-519, 2003.
[30] De Luca, C. J., Gilmore, L. D., Kuznetsov, M., and Roy, S. H., "Filtering the surface EMG signal: Movement artifact and baseline noise contamination," Journal of biomechanics, Vol.43, No.8, pp.1573-1579, 2010.
[31] Potvin, J. and Brown, S., "Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates," Journal of Electromyography and Kinesiology, Vol.14, No.3, pp.389-399, 2004.
[32] Wang, J., Tang, L., and Bronlund, J. E., "Surface EMG signal amplification and filtering," International Journal of Computer Applications, Vol.82, No.1, pp.15-22, 2013.
[33] Huigen, E., Peper, A., and Grimbergen, C., "Investigation into the origin of the noise of surface electrodes," Medical and biological engineering and computing, Vol.40, No.3, pp.332-338, 2002.
[34] Clancy, E. A., Morin, E. L., and Merletti, R., "Sampling, noise-reduction and amplitude estimation issues in surface electromyography," Journal of Electromyography and Kinesiology, Vol.12, No.1, pp.1-16, 2002.
[35] Stegeman, D. and Hermens, H., Standards for surface electromyography: The European project Surface EMG for non-invasive assessment of muscles (SENIAM), Enschede: Roessingh Research and Development, 2007.
[36] Clancy, E. A., Bouchard, S., and Rancourt, D., "Estimation and application of EMG amplitude during dynamic contractions," IEEE Engineering in Medicine and Biology Magazine, Vol.20, No.6, pp.47-54, 2001.
[37] Bonato, P., D'Alessio, T., and Knaflitz, M., "A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait," IEEE Transactions on biomedical engineering, Vol.45, No.3, pp.287-299, 1998.
[38] De Luca, C. J. and Knaflitz, M., Surface electromyography: What's new?, CLUT Torino, 1992.
[39] Evans, H. B., Pan, Z., Parker, P. A., and Scott, R. N., "Signal processing for proportional myoelectric control," IEEE Transactions on biomedical engineering, No.2, pp.207-211, 1984.
[40] Zardoshti-Kermani, M., Wheeler, B. C., Badie, K., and Hashemi, R. M., "EMG feature evaluation for movement control of upper extremity prostheses," IEEE Transactions on Rehabilitation Engineering, Vol.3, No.4, pp.324-333, 1995.
[41] Merletti, R. and Parker, P. J., Electromyography: physiology, engineering, and non-invasive applications, John Wiley & Sons, 2004.
[42] Edwards, R. H., "Human muscle function and fatigue," Physiological Mechanisms, Vol.82, pp.1-18, 1981.
[43] Enoka, R. M., "Mechanisms of muscle fatigue: central factors and task dependency," Journal of Electromyography and Kinesiology, Vol.5, No.3, pp.141-149, 1995.
[44] Cifrek, M., Medved, V., Tonković, S., and Ostojić, S., "Surface EMG based muscle fatigue evaluation in biomechanics," Clinical biomechanics, Vol.24, No.4, pp.327-340, 2009.
[45] Cobb, S. and Forbes, A., "Electromyographic studies of muscular fatigue in man," American Journal of Physiology-Legacy Content, Vol.65, No.2, pp.234-251, 1923.
[46] De Luca, C. J., "Myoelectrical manifestations of localized muscular fatigue in humans," Critical reviews in biomedical engineering, Vol.11, No.4, pp.251-279, 1984.
[47] Kogi, K. and Hakamada, T., "Frequency analysis of the surface electromyogram in muscle fatigue," The Journal of science of labour, Vol.38, pp.519-528, 1962.
[48] Lindstrom, L., "Muscular fatigue and action potential conduction velocity changes studied with frequency analysis of EMG signals," Electromyography, Vol.10, No.4, pp.341-356, 1970.
[49] Kwatny, E., Thomas, D. H., and Kwatny, H. G., "An application of signal processing techniques to the study of myoelectric signals," IEEE Transactions on biomedical engineering, No.4, pp.303-313, 1970.
[50] D'Alessio, T., Knaflitz, M., Balestra, G., and Paggi, S., "On-line estimation of myoelectric signal spectral parameters and nonstationarities detection," IEEE Transactions on biomedical engineering, Vol.40, No.9, pp.981-985, 1993.
[51] Pratt, A., Gander, R., and Brandell, B., "Real-time digital median frequency estimator for surface myoelectric signals," IEEE Transactions on biomedical engineering, Vol.38, No.3, pp.306-309, 1991.
[52] Seroussi, R., Krag, M. H., Wilder, P., and Pope, M. H., "The design and use of a microcomputerized real-time muscle fatigue monitor based on the medical frequency shift in the electromyographic signal," IEEE Transactions on biomedical engineering, Vol.36, No.2, pp.284-286, 1989.
[53] Kramer, C., Hagg, T., and Kemp, B., "Real-time measurement of muscle fatigue related changes in surface EMG," Medical and biological engineering and computing, Vol.25, No.6, pp.627-630, 1987.
[54] Karlsson, S., Yu, J., and Akay, M., "Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study," IEEE Transactions on biomedical engineering, Vol.47, No.2, pp.228-238, 2000.
[55] Cifrek, M., Tonković, S., and Medved, V., "Measurement and analysis of surface myoelectric signals during fatigued cyclic dynamic contractions," Measurement, Vol.27, No.2, pp.85-92, 2000.
[56] Lowery, M., Nolan, P., and O’malley, M., "Electromyogram median frequency, spectral compression and muscle fibre conduction velocity during sustained sub-maximal contraction of the brachioradialis muscle," Journal of Electromyography and Kinesiology, Vol.12, No.2, pp.111-118, 2002.
[57] Lowery, M. M., Vaughan, C. L., Nolan, P. J., and O'Malley, M. J., "Spectral compression of the electromyographic signal due to decreasing muscle fiber conduction velocity," IEEE Transactions on Rehabilitation Engineering, Vol.8, No.3, pp.353-361, 2000.
[58] 蔡政龍, "肌電圖強度與速度分析於機器手臂控制之應用," 國立交通大學電控工程研究所碩士論文, 2005.
[59] Felici, F., Rosponi, A., Sbriccoli, P., Filligoi, G., Fattorini, L., and Marchetti, M., "Linear and non-linear analysis of surface electromyograms in weightlifters," European journal of applied physiology, Vol.84, No.4, pp.337-342, 2001.
[60] Merletti, R., Farina, D., and Gazzoni, M., "The linear electrode array: a useful tool with many applications," Journal of Electromyography and Kinesiology, Vol.13, No.1, pp.37-47, 2003.
[61] Farina, D., Merletti, R., and Disselhorst-Klug, C., "Multi-channel techniques for information extraction from the surface EMG," Electromyography: Physiology, engineering, and non-invasive applications, pp.169-203, 2004.
[62] Delsys, "Trigno Avanti Platform," <https://delsys.com/trigno/>, accessed on 3/20/2021
[63] Corp., B. B., "FREEEMG," <https://www.btsbioengineering.com/products/freeemg-surface-emg-semg/>, accessed on 5/25/2/21
[64] mbH, E. H. G., "SURPASS II," <https://emsbiomed.com/products/emg-ep/surpass-ii>, accessed on 5/25/2021
[65] Tools, B. S., "Skybox," <https://www.brainsciencetools.com/en/product/skybox-5-channel-portable-emg-ncs-and-ep-system/>, accessed on 5/25/2021
[66] ACERTYS, "Nicolet® VikingQuest® EMG / NCS / EP System," <https://www.acertys.com/en/emgep/nicoletr-vikingquestr-emg-ncs-ep-system>, accessed on 5/25/2021
[67] GmbH, S. M.-T., "NEUROWERK EMG portable," <https://www.medicalexpo.com/prod/sigma-medizin-technik-gmbh/product-70079-807981.html>, accessed on 5/25/2021
[68] Shimmer, "Shimmer3 EMG Unit," <https://www.shimmersensing.com/products/shimmer3-emg-sensor>, accessed on 5/25/2021
[69] Richer, R., Blank, P., Schuldhaus, D., and Eskofier, B. M., "Real-time ECG and EMG analysis for biking using android-based mobile devices," in 2014 11th International Conference on Wearable and Implantable Body Sensor Networks, pp.104-108, 2014.
[70] MathWorks, "MATLAB," <https://www.mathworks.com/products/matlab.html>, accessed on 6/12/2021
[71] Widasari, E. R., Miyauchi, R., Tamura, H., and Tanno, K., "A wireless surface electromyogram monitoring system using smartphone and its application to maintain biceps muscle," IEEE International Conference on Systems, Man, and Cybernetics, pp.2378-2383, 2015.
[72] C. J. Chen, K. C. L., "Fiber Composite Silver Chloride Electrochemical Modification of Method," Journal of the Hwa Gang Textile, Vol.20, No.1, pp.35-39, 2013.
[73] Health, C., "Foam Monitoring Electrodes," <https://www.cardinalhealth.com/en/product-solutions/medical/patient-monitoring/electrocardiography/monitoring-ecg-electrodes/foam-monitoring-electrodes.html>, accessed on 3/28/2021
[74] Giustolisi, G., Palmisano, G., and Palumbo, G., "CMRR frequency response of CMOS operational transconductance amplifiers," IEEE Transactions on instrumentation and Measurement, Vol.49, No.1, pp.137-143, 2000.
[75] DFRobot, "Gravity: Analog EMG Sensor by OYMotion," <https://www.dfrobot.com/product-1661.html>, accessed on 3/28/2021
[76] Seeed, "Grove - EMG Detector," <https://wiki.seeedstudio.com/Grove-EMG_Detector/>, accessed on 3/28/2021
[77] Technology, A., "MyoWare Muscle Sensor," <http://www.advancertechnologies.com/p/myoware.html>, accessed on 3/28/2021
[78] Pi台灣樹莓派, R., "Raspberry Pi 樹莓派 UPS 鋰電池擴充板," <https://www.raspberrypi.com.tw/34752/raspberry-pi-ups-lithium-battery-expansion-board-v3/>, accessed on 3/29/2021
[79] Arduino, "Arduino Nano," <https://store.arduino.cc/usa/arduino-nano>, accessed on 3/29/2021
[80] Pi, R., "Raspberry Pi 3 Model B+," <https://www.raspberrypi.org/products/raspberry-pi-3-model-b-plus/>, accessed on 3/30/2021
[81] connects, W. T., "With 30 lines over the network: TCP/IP sockets as a programming interface," <https://www.wut.de/e-58www-16-apus-000.php>, accessed on 4/1/2021
[82] Landau, H., "Sampling, data transmission, and the Nyquist rate," Proceedings of the IEEE, Vol.55, No.10, pp.1701-1706, 1967.
[83] Frenzel, L., "What’s the difference between bit rate and baud rate," Electron. Des., Vol.20, p.12, 2012.
[84] Antonion, A., "Digital Filters: Analysis, Design and Application," McGraw Hill, New York, Vol.5, p.886896, 1993.
[85] Damato, A., "Electronic linear filters," <https://commons.wikimedia.org/wiki/File:Electronic_linear_filters.svg>, accessed on 5/25/2021
[86] Liu, S.-H., Lin, C.-B., Chen, Y., Chen, W., Huang, T.-S., and Hsu, C.-Y., "An EMG patch for the real-time monitoring of muscle-fatigue conditions during exercise," Sensors, Vol.19, No.14, p.3108, 2019.
[87] Tektronix, "Arbitrary Function Generators AFG310," <https://tw.tek.com/datasheet/afg310-afg320>, accessed on 5/20/2021
[88] Instek, G., "GW Instek GDS-1022," <https://www.gwinstek.com/en-global/products/detail/GBS-1022>, accessed on 5/21/2021
[89] Udovičić, M., Baždarić, K., Bilić-Zulle, L., and Petrovečki, M., "What we need to know when calculating the coefficient of correlation?," Biochemia Medica, Vol.17, No.1, pp.10-15, 2007.
[90] Gollhoferl, A., Horstmann, G., Schmidtbleicher, D., and Schönthall, D., "Reproducibility of electromyographic patterns in stretch-shortening type contractions," European journal of applied physiology and occupational physiology, Vol.60, No.1, pp.7-14, 1990.
[91] Pavlat, D. J., Housh, T. J., Johnson, G. O., Schmidt, R. J., and Eckerson, J. M., "An examination of the electromyographic fatigue threshold test," European journal of applied physiology and occupational physiology, Vol.67, No.4, pp.305-308, 1993.
[92] Hendrix, C. R., Housh, T. J., Johnson, G. O., Mielke, M., Camic, C. L., Zuniga, J. M., and Schmidt, R. J., "A new EMG frequency-based fatigue threshold test," Journal of neuroscience methods, Vol.181, No.1, pp.45-51, 2009.
[93] Karthick, P., Ghosh, D. M., and Ramakrishnan, S., "Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms," Computer methods and programs in biomedicine, Vol.154, pp.45-56, 2018.