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Surface EMG: Applicability in the Motion Analysis and Opportunities for Practical Rehabilitation

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Abstract

Surface electromyography (EMG) has long since entered the routine of both clinicians and researchers, providing simple and objective quantitative assessment of the function of the muscles and peripheral nervous system. The vast majority of modern high-tech rehabilitation and assistive technologies use surface EMG as a feedback source or exoskeleton drive controller. For all years of its use, surface EMG has not become a versatile method, having encountered a number of obstacles, both in science and in clinical practice. However, the development of technologies, combined use together with other methods, and large number of studies aimed at improving the method has allowed surface EMG to become an indispensable tool in biomechanical analysis of movements and practical rehabilitation. This review describes the main advantages and disadvantages, research results, and clinical use of surface EMG.

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REFERENCES

  1. Disselhorst-Klug, C., Schmitz-Rode, T., and Rau, G., Surface electromyography and muscle force: limits in sEMG-force relationship and new approaches for applications, Clin. Biomech., 2009, vol. 24, no. 3, p. 225.

    Article  Google Scholar 

  2. Rukina, N.N., Kuznetsov, A.N., Borzikov, V.V., et al., Surface electromyography: Its role and potential in the development of exoskeleton (review), Sovrem. Tehnol. Med., 2016, vol. 8, no. 2, p. 109.

    Article  Google Scholar 

  3. Gekht, B.M., Teoreticheskaya i klinicheskaya elektromiografiya (Theoretical and Clinical Electromyography), Leningrad: Nauka, 1990.

  4. Chaffin, D.B., Surface electromyography frequency analysis as a diagnostic tool, J. Occup. Med., 1969, vol. 11, no. 3, p. 109.

    CAS  PubMed  Google Scholar 

  5. Hagberg, M. and Ericson, B.E., Myoelectric power spectrum dependence on muscular contraction level of elbow flexors, Eur. J. Appl. Physiol. Occup. Physiol., 1982, vol. 48, no. 2, p. 147.

    Article  CAS  PubMed  Google Scholar 

  6. Muro, M., Nagata, A., Murakami, K., and Moritani, T., EMG power spectral analysis of neuro-muscular disorder patients during isometric and isotonic contractions, Am. J. Phys. Med. Rehabil., 1982, vol. 61, no. 5, p. 99.

    Google Scholar 

  7. Rainoldi, A., Galardi, G., Maderna, L., et al., Repeatability of surface EMG variables during voluntary isometric contractions of the biceps brachii muscle, J. Electromyogr. Kinesiol., 1999, vol. 9, no. 2, p. 105.

    Article  CAS  PubMed  Google Scholar 

  8. Farina, D., Fosciand, M., and Merletti, R., Motor unit recruitment strategies investigated by surface EMG variables, J. Appl. Physiol., 2002, vol. 92, no. 1, p. 235.

    Article  PubMed  Google Scholar 

  9. The State of the Art on Signal Processing Methods for Surface ElectroMyoGraphy, Hermens, H., Freriks, B., Merletti, R., and Rix, H., Eds., Enschede: Roessingh Res. Dev., 1999, p. 4.

    Google Scholar 

  10. Hermens, H.J., Freriks, B., Disselhorst-Klug, C., and Rau, G., Development of recommendations for SEMG sensors and sensor placement procedures, J. Electromyogr. Kinesiol., 2000, vol. 10, no. 5, p. 361.

    Article  CAS  PubMed  Google Scholar 

  11. Cram, J.R., Kasman, G.S., and Holtz, J., Introduction to Surface Electromyography, Gaithersburg, MD: Aspen, 1998.

    Google Scholar 

  12. Basmajian, J. and De Luca, C.J., Description and analysis of the EMG signal, in Muscles Alive, Their Functions Revealed by Electromyography, Baltimore: Williams & Wilkins, 1985, p. 561.

    Google Scholar 

  13. Pah, N. and Kumar, D.K., Classification of electromyograph for localised muscle fatigue using neural networks, Proc. 7th Australian and New Zealand Intelligent Information Systems Conf. (ANZIIS 2001), Piscataway, NJ: Inst. Electr. Electron. Eng., 2001, p. 271.

  14. Fleischer, C., Wege, A., Kondak, K., and Hommel, G., Application of EMG signals for controlling exoskeleton robots, Biomed. Tech., 2006, vol. 51, nos. 5–6, p. 314.

    Article  Google Scholar 

  15. Merletti, R., Aventaggiato, M., Botter, A., et al., Advances in surface EMG: recent progress in detection and processing techniques, Crit. Rev. Biomed. Eng., 2010, vol. 38, no. 4, p. 305.

    Article  PubMed  Google Scholar 

  16. Chowdhury, R.H., Reaz, M.B., Ali, M.A., et al., Surface electromyography signal processing and classification techniques, Sensors, 2013, vol. 13, no. 9, p. 12431.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kellis, E., Quantification of quadriceps and hamstring antagonist activity, Sports Med, 1998, vol. 25, no. 1, p. 37.

    Article  CAS  PubMed  Google Scholar 

  18. Holtermann, A., Roeleveld, K., and Karlsson, J.S., Inhomogeneities in muscle activation reveal motor unit recruitment, J. Electromyogr. Kinesiol., 2005, vol. 15, no. 2, p. 131.

    Article  PubMed  Google Scholar 

  19. Byrne, C.A., Lyons, G.M., Donnelly, A.E., et al., Rectus femoris surface myoelectric signal cross-talk during static contractions, J. Electromyogr. Kinesiol., 2005, vol. 15, no. 6, p. 564.

    Article  CAS  PubMed  Google Scholar 

  20. Ginn, K.A. and Halaki, M., Do surface electrode recordings validly represent latissimus dorsi activation patterns during shoulder tasks? J. Electromyogr. Kine-siol., 2015, vol. 25, no. 1, p. 8.

    Article  Google Scholar 

  21. Barr, K.M., Miller, A.L., and Chapin, K.B., Surface electromyography does not accurately reflect rectus femoris activity during gait: Impact of speed and crouch on vasti-to-rectus crosstalk, Gait Posture, 2010, vol. 32, no. 3, p. 363.

    Article  PubMed  Google Scholar 

  22. Nene, A., Byrne, C., and Hermens, H., Is rectus femoris really a part of quadriceps? Gait Posture, 2004, vol. 20, no. 1, p. 1.

    Article  CAS  PubMed  Google Scholar 

  23. Gallina, A., Peters, S., Neva, J.L., et al., Selectivity of conventional electrodes for recording motor evoked potentials: An investigation with high-density surface electromyography, Muscle Nerve, 2017, vol. 55, no. 6, p. 828.

    Article  PubMed  Google Scholar 

  24. Jiroumaru, T., Kurihara, T., and Isaka, T., Establishment of a recording method for surface electromyography in the iliopsoas muscle, J. Electromyogr. Kinesiol., 2014, vol. 24, no. 4, p. 445.

    Article  PubMed  Google Scholar 

  25. Lowery, M.M., Stoykov, N.S., and Kuiken, T.A., A simulation study to examine the use of cross-correlation as an estimate of surface EMG cross talk, J. Appl. Physiol., 2003, vol. 94, no. 4, p. 1324.

    Article  PubMed  Google Scholar 

  26. Naik, G.R., Guo, Y., and Nguyen, H.T., A new approach to improve the quality of biosensor signals using fast independent component analysis: feasibility study using EMG recordings, Proc. 35th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), Piscataway, NJ: Inst. Electr. Electron. Eng., 2013, p. 1927.

  27. Dimitrova, N.A., Dimitrov, G.V., and Nikitin, O.A., Neither high-pass filtering nor mathematical differentiation of the EMG signals can considerably reduce cross-talk, J. Electromyogr. Kinesiol., 2002, vol. 12, no. 4, p. 235.

    Article  CAS  PubMed  Google Scholar 

  28. Merletti, R. and Parker, P., Electromyography: Physiology, Engineering, and Non-Invasive Applications, Chichester: Wiley, 2004.

    Book  Google Scholar 

  29. Hug, F., Can muscle coordination be precisely studied by surface electromyography? J. Electromyogr. Kinesiol., 2011, vol. 21, no. 1, p. 1.

    Article  PubMed  Google Scholar 

  30. Gabiccini, M., Stillfried, G., Marino, H., and Bianchi, M., A data-driven kinematic model of the human hand with soft-tissue artifact compensation mechanism for grasp synergy analysis, Proc. IEEE Int. Conf. on Intelligent Robots and Systems, Piscataway, NJ: Inst. Electr. Electron. Eng., 2013, p. 3738.

  31. Tessitore, G., Sinigaglia, C., and Prevete, R., Hierarchical and multiple hand action representation using temporal postural synergies, Exp. Brain Res., 2013, vol. 225, no. 1, p. 11.

    Article  CAS  PubMed  Google Scholar 

  32. D’Avella, A., Saltiel, P., and Bizzi, E., Combinations of muscle synergies in the construction of a natural motor behavior, Nat. Neurosci., 2003, vol. 6, no. 3, p. 300.

    Article  PubMed  CAS  Google Scholar 

  33. Weiss, E.J. and Flanders, M., Muscular and postural synergies of the human hand, J. Neurophysiol., 2004, vol. 92, no. 1, p. 523.

    Article  PubMed  Google Scholar 

  34. Klochkov, A.S., Khizhnikova, A.E., Nazarova, M.A., and Chernikova, L.A., Pathological upper limb synergies of patients with poststroke hemiparesis, Neurosci. Behav. Physiol., 2018, vol. 48, no. 2, pp. 813–822.

    Article  Google Scholar 

  35. Klein Breteler, M.D., Simura, K.J., and Flanders, M., Timing of muscle activation in a hand movement sequence, Cereb. Cortex, 2007, vol. 17, no. 4, p. 803.

    Article  PubMed  Google Scholar 

  36. Artemiadis, P.K. and Kyriakopoulos, K.J., Teleoperation of a robot manipulator using EMG signals and a position tracker, Proc. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Piscataway, NJ: Inst. Electr. Electron. Eng., 2005, p. 3480.

  37. Smith, R.J., Tenore, F., Huberdeau, D., et al., Continuous decoding of finger position from surface EMG signals for the control of powered prostheses, Proc. 30th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBS’08) “Personalized Healthcare through Technology,” Piscataway, NJ: Inst. Electr. Electron. Eng., 2008, p. 197.

  38. Falconer, K., Quantitative assessment of co-contraction at the ankle joint in walking, Electromyogr. Clin. Neurophysiol., 1985, vol. 25, nos. 2–3, p. 135.

    CAS  PubMed  Google Scholar 

  39. Hortobágyi, T., Solnik, S., Gruber, A., et al., Interaction between age and gait velocity in the amplitude and timing of antagonist muscle coactivation, Gait Posture, 2009, vol. 29, no. 4, p. 558.

    Article  PubMed  Google Scholar 

  40. Franz, J.R. and Kram, R., How does age affect leg muscle activity/coactivity during uphill and downhill walking? Gait Posture, 2013, vol. 37, no. 3, p. 378.

    Article  PubMed  Google Scholar 

  41. Sutherland, D.H., The evolution of clinical gait analysis part l: kinesiological EMG, Gait Posture, 2001, vol. 14, no. 1, p. 61.

    Article  CAS  PubMed  Google Scholar 

  42. Kim, J.H., The effects of training using EMG biofeedback on stroke patients upper extremity functions, J. Phys. Ther. Sci. Soc., 2017, vol. 29, no. 6, p. 1085.

    Article  Google Scholar 

  43. Cirstea, M.C. and Levin, M.F., Compensatory strategies for reaching in stroke, Brain, 2000, vol. 123, no. 5, p. 940.

    Article  PubMed  Google Scholar 

  44. Ma, K., Chen, Y., Zhang, X., et al., sEMG-based trunk compensation detection in rehabilitation training, Front. Neurosci., 2019, vol. 13, p. 1250.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Wee, S.K., Hughes, A.M., Warner, M., and Burridge, J.H., Trunk restraint to promote upper extremity recovery in stroke patients: a systematic review and meta-analysis, Neurorehabil. Neural Repair, 2014, vol. 28, no. 7, p. 660.

    Article  PubMed  Google Scholar 

  46. Pain, L.M., Baker, R., Richardson, D., and Agur, A.M.R., Effect of trunk-restraint training on function and compensatory trunk, shoulder and elbow patterns during post-stroke reach: a systematic review, Disabil. Rehabil. Inf. Healthcare, 2015, vol. 37, no. 7, p. 553.

    Article  Google Scholar 

  47. Greisberger, A., Aviv, H., Garbade, S.F., and Diermayr, G., Clinical relevance of the effects of reach-to-grasp training using trunk restraint in individuals with hemiparesis poststroke: a systematic review, J. Rehabil. Med., 2016, vol. 48, no. 5, p. 405.

    Article  PubMed  Google Scholar 

  48. Romkes, J., Rudmann, C., and Brunner, R., Changes in gait and EMG when walking with the Masai barefoot technique, Clin. Biomech., 2006, vol. 2, no. 1, p. 75.

    Article  Google Scholar 

  49. Murley, G.S., Landorf, K.B., Menz, H.B., and Bird, A.R., Effect of foot posture, foot orthoses and footwear on lower limb muscle activity during walking and running: a systematic review, Gait Posture, 2009, vol. 29, no. 2, p. 172.

    Article  PubMed  Google Scholar 

  50. Neblett, R., Mayer, T.G., Brede, E., and Gatchel, R.J., Correcting abnormal flexion-relaxation in chronic lumbar pain: responsiveness to a new biofeedback training protocol, Clin. J. Pain, 2010, vol. 26, no. 5, p. 403.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Geisser, M.E., Ranavaya, M., Haig, A.J., et al., A meta-analytic review of surface electromyography among persons with low back pain and normal, healthy controls, J. Pain, 2005, vol. 6, no. 11, p. 711.

    Article  PubMed  Google Scholar 

  52. Narayanan, S.P. and Bharucha, A.E., A practical guide to biofeedback therapy for pelvic floor disorders, Curr. Gastroenterol. Rep., 2019, vol. 21, no. 5, p. 21.

    Article  PubMed  Google Scholar 

  53. Criado, L., de La Fuente, A., Heredia, M., et al., Electromyographic biofeedback training for reducing muscle pain and tension on masseter and temporal muscles: a pilot study, J. Clin. Exp. Dent., 2016, vol. 8, no. 5, p. e571.

    PubMed  PubMed Central  Google Scholar 

  54. Mur, E., Drexler, A., Gruber, J., et al., Electromyography biofeedback therapy in fibromyalgia, Wien. Med. Wochenschr., 1999, vol. 149, nos. 19–20, p. 561.

    CAS  PubMed  Google Scholar 

  55. Kolbe, L., Eberhardt, T., Leinberger, B., and Hinterberger, T., Effectiveness of biofeedback for primary headache: a randomized controlled study, Psychother., Psychosomatik Med. Psychol., 2020, vol. 70, no. 7, p. 300.

    Google Scholar 

  56. Shtark, M.B., The biofeedback technology: research and practice, Byull. Sib. Otd., Ross. Akad. Med. Nauk, 2004, no. 3, p. 8.

  57. Chernikova, L.A., Ioffe, M.E., Busheneva, S.N., et al., MG biofeedback and functional magnetic resonance imaging in the post-stroke rehabilitation (precise grip training), Byull. Sib. Med., 2010, vol. 9, no. 2, p. 12.

    Article  Google Scholar 

  58. Aprile, I., Germanotta, M., Cruciani, A., et al., Upper limb robotic rehabilitation after stroke: a multicenter, randomized clinical trial, J. Neurol. Phys. Ther., 2020, vol. 44, no. 1, p. 3.

    Article  PubMed  Google Scholar 

  59. Bong, J.H., Jung, S., Park, N., et al., Development of a novel robotic rehabilitation system with muscle-to-muscle interface, Front. Neurorob., 2020, vol. 14, p. 3.

    Article  Google Scholar 

  60. Kim, G.J., Taub, M., Creelman, C., et al., Feasibility of an electromyography-triggered hand robot for people after chronic stroke, Am. J. Occup. Ther., 2019, vol. 73, no. 4, p. 7304345040p1.

  61. Meattini, R., Biagiotti, L., Palli, G., et al., A control architecture for grasp strength regulation in myocontrolled robotic hands using vibrotactile feedback: preliminary results, Proc. IEEE Int. Conf. on Rehabilitation Robotics, June 24–28, 2019, Piscataway, NJ: Inst. Electr. Electron. Eng., 2019, vol. 2019, p. 1272.

  62. Woodford, H. and Price, C., EMG biofeedback for the recovery of motor function after stroke, Cochrane Database Syst. Rev., 2007, vol. 2007, no. 2, p. CD004585.

    PubMed Central  Google Scholar 

  63. Moreau, N.G., Bodkin, A.W., Bjornson, K., et al., Effectiveness of rehabilitation interventions to improve gait speed in children with cerebral palsy: systematic review and meta-analysis, Phys. Ther., 2016, vol. 96, no. 12, p. 1938.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Moreland, J.D., Thomson, M.A., and Fuoco, A.R., Electromyographic biofeedback to improve lower extremity function after stroke: a meta-analysis, Arch. Phys. Med. Rehabil., 1998, vol. 79, no. 2, p. 134.

    Article  CAS  PubMed  Google Scholar 

  65. Sadler, C.M. and Cressman, E.K., Central fatigue mechanisms are responsible for decreases in hand proprioceptive acuity following shoulder muscle fatigue, Hum. Mov. Sci., 2019, vol. 66, p. 220.

    Article  PubMed  Google Scholar 

  66. Karagiannopoulos, C., Watson, J., Kahan, S., and Lawler, D., The effect of muscle fatigue on wrist joint position sense in healthy adults, J. Hand Ther., 2019, vol. 33, no. 3, p. 329.

    Article  PubMed  Google Scholar 

  67. Song, G.B., The effects of task-oriented versus repetitive bilateral arm training on upper limb function and activities of daily living in stroke patients, J. Phys. Ther. Sci., 2015, vol. 27, no. 5, p. 1353.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Shahar, N., Schwartz, I., and Portnoy, S., Differences in muscle activity and fatigue of the upper limb between task-specific training and robot assisted training among individuals post stroke, J. Biomech., 2019, vol. 89, p. 28.

    Article  PubMed  Google Scholar 

  69. Sze, W.P., Yoon, W.L., Escoffier, N., and Liow, S.J.R., Evaluating the training effects of two swallowing rehabilitation therapies using surface electromyography—chin tuck against resistance (CTAR) exercise and the shaker exercise, Dysphagia, 2016, vol. 31, no. 2, p. 195.

    Article  PubMed  Google Scholar 

  70. Dipietro, L., Ferraro, M., Palazzolo, J.J., et al., Customized interactive robotic treatment for stroke: EMG-triggered therapy, IEEE Trans. Neural Syst. Rehabil. Eng., 2005, vol. 13, no. 3, p. 325.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Chan, B.S., Sia, C.L., Wong, F., et al., Analysis of surface electromyography for on-off control, Adv. Mater. Res., 2013, vol. 701, p. 435.

    Article  Google Scholar 

  72. Song, R., Tong, K.Y., Hu, X., and Li, L., Assistive control system using continuous myoelectric signal in robot-aided arm training for patients after stroke, IEEE Trans. Neural Syst. Rehabil. Eng., 2008, vol. 16, no. 4, p. 371.

    Article  PubMed  Google Scholar 

  73. Tang, Z., Zhang, K., Sun, S., et al., An upper-limb power-assist exoskeleton using proportional myoelectric control, Sensors, 2014, vol. 14, no. 4, p. 6677.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Vorob’ev, A.A., Petrukhin, A.V., Krivonozhkina, P.S., and Pozdnyakov, A.M., Exoskeleton as a new means in habilitation and rehabilitation of invalids (review), Sovrem. Tehnol. Med., 2015, vol. 7, no. 2, p. 185.

    Article  Google Scholar 

  75. Fougner, A., Stavdahl, O., Kyberd, P.J., et al., Control of upper limb prostheses: terminology and proportional myoelectric controla review, IEEE Trans. Neural Syst. Rehabil. Eng., 2012, vol. 20, no. 5, p. 663.

    Article  PubMed  Google Scholar 

  76. Pistohl, T., Cipriani, C., Jackson, A., and Nazarpour, K., Adapting proportional myoelectric-controlled interfaces for prosthetic hands, Proc. 35th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, Piscataway, NJ: Inst. Electr. Electron. Eng., 2013, p. 6195.

  77. Ferris, D.P. and Lewis, C.L., Robotic lower limb exoskeletons using proportional myoelectric control, Proc. 31st Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society “Engineering the Future of Biomedicine,” September 3–6, 2009, Piscataway, NJ: Inst. Electr. Electron. Eng., 2009, p. 2119.

  78. Guizzo, E. and Goldstein, H., The rise of the body bots (robotic exoskeletons), IEEE Spectrum, 2005, vol. 42, no. 10, p. 50.

    Article  Google Scholar 

  79. Lenzi, T., De Rossi, S.M.M., Vitiello, N., and Carrozza, M.C., Intention-based EMG control for powered exoskeletons, IEEE Trans. Biomed. Eng., 2012, vol. 59, no. 8, p. 2180.

    Article  CAS  PubMed  Google Scholar 

  80. Gao, B., Wei, C., Ma, H., et al., Real-time evaluation of the signal processing of sEMG used in limb exoskeleton rehabilitation system, Appl. Bionics Biomech., 2018, vol. 2018, art. ID 1391032.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Lu, Z., Stampas, A., Francisco, G.E., and Zhou, P., Offline and online myoelectric pattern recognition analysis and real-time control of a robotic hand after spinal cord injury, J. Neural Eng., 2019, vol. 16, no. 3, p. 036018.

    Article  PubMed  Google Scholar 

  82. Rosen, J., Brand, M., Fuchs, M.B., and Arcan, M., A myosignal-based powered exoskeleton system, IEEE Trans. Syst., Man, Cybern., Part A, 2001, vol. 31, no. 3, p. 210.

    Google Scholar 

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Kotov-Smolenskiy, A.M., Khizhnikova, A.E., Klochkov, A.S. et al. Surface EMG: Applicability in the Motion Analysis and Opportunities for Practical Rehabilitation. Hum Physiol 47, 237–247 (2021). https://doi.org/10.1134/S0362119721020043

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