Advertisement

Surface EMG in Neurorehabilitation and Ergonomics: State of the Art and Future Perspectives

  • Filipe BarrosoEmail author
  • Diana Ruiz Bueno
  • Juan Álvaro Gallego
  • Paola Jaramillo
  • Atilla Kilicarslan
Chapter
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 4)

Abstract

Electromyography is a valuable technique that can be used for several purposes, including the comprehension and assessment of the motor system as well as the diagnosis of some pathologies and rehabilitation. Given the drawbacks of traditional surface electromyography recordings with two electrodes, a new approach called high-density surface electromyography enables implementation of spatial information to the temporal information content of the electromyographic signal. The following review describes the rationale for the use of high-density recordings, the state of the art techniques, and technologies for its detection and conditioning. Some examples are showcased providing new insights on muscle physiology, ergonomics (for the assessment and prevention of musculoskeletal disorders), as well as training and rehabilitation treatments.

Keywords

High-density surface electromyography Motor units Muscle physiology Ergonomics Work-related disorders 

Notes

Acknowledgments

The authors thank Prof. Roberto Merletti (LISiN, Politecnico di Torino, Torino, Italy) for his valuable help in the organization of the chapter, his contribution to the writing of this chapter, and his subsequent revisions of the text.

The authors also thank Diego Torricelli for his continuous supervising, always paying attention to the detail and giving important advices to work out this chapter.

This chapter is partially based on the plenary lecture “Prevention and rehabilitation of neuromuscular disorders using High Density Surface EMG” imparted by Prof Roberto Merletti at the 2012 International Summer School on Neurorehabilitation, “Emerging Therapies,” held in Zaragoza from the 16th to the 21st of September 2012.

References

  1. Adrian ED, Bronk DW (1929) The discharge of impulses in motor nerve fibres: Part II. The frequency of discharge in reflex and voluntary contractions. J Physiol 67(2):i3–151Google Scholar
  2. Barbero M, Merletti R, Rainoldi A (2012) Atlas of muscle innervation zones: understanding surface electromyography and its applications. Springer, MilanGoogle Scholar
  3. Basmajian JV, De Luca CJ (1985) Muscles alive: their functions revealed by electromyography, 5th edn. William and Wilkins, BaltimoreGoogle Scholar
  4. Bonfiglioli R, Botter A, Calabrese M, Mussoni P, Violante FS, Merletti R (2012) Surface electromyography features in manual workers affected by carpal tunnel syndrome. Muscle Nerve 45(6):873–882Google Scholar
  5. Broman H, Billotto G, De Luca CJ (1985) A note on noninvasive estimation of muscle fiber conduction velocity. IEEE Trans Biomed Eng 32:341–344Google Scholar
  6. Cescon C, Bottin A, Fernandez-Fraga XL, Azpiroz F, Merletti R (2008) Detection of individual motor units of the puborectalis muscle by non-invasive EMG electrode arrays. J Electromyogr Kinesiol 18(3):382–389CrossRefGoogle Scholar
  7. Cescon C, Mesin L, Nowakowski M, Merletti R (2011) Geometry assessment of anal sphincter muscle based on monopolar multichannel surface EMG signals. J Electromyogr Kinesiol 21(2):394–401CrossRefGoogle Scholar
  8. De Luca CJ (1997) The use of surface electromyography in biomechanics. J Appl Biomech 13:135–163Google Scholar
  9. Enck P, Franz H, Azpiroz F, Fernandez-Fraga X, Hinninghofen H, Kaske-Bretag K, Bottin A, Martina S, Merletti R (2004) Innervation zones of the external anal sphincter in healthy male and female subjects. Preliminary results. Digestion 69(2):123–130CrossRefGoogle Scholar
  10. Farina D, Fortunato E, Merletti R (2000) Noninvasive estimation of motor unit conduction velocity distribution using linear electrode arrays. IEEE Trans Biomed Eng 47:380–388CrossRefGoogle Scholar
  11. Farina D, Cescon C (2001) Concentric ring electrode system for non-invasive detection of single motor unit activity. IEEE Trans Biomed Eng 48:1326–1334Google Scholar
  12. Farina D, Holobar A, Merletti R, Enoka RM (2010) Decoding the neural drive to muscle from the surface electromyogram. Clin Neurophysiol 121:1616–1623CrossRefGoogle Scholar
  13. Farina D, Merletti R, Enoka RM (2004) The extraction of neural strategies from the surface EMG. J Appl Physiol 96:1486–1495CrossRefGoogle Scholar
  14. Farina D, Negro F, Gazzoni M, Enoka RM (2008a) Detecting the unique representation of motor-unit action potentials in the surface electromyogram. J Neurophysiol 100:1223–1233CrossRefGoogle Scholar
  15. Farina D, Yoshida K, Stieglitz T, Koch KP (2008b) Multichannel thin-film electrode for intramuscular electromyographic recordings. J Appl Physiol 104(3):821–827CrossRefGoogle Scholar
  16. Gazzoni M (2010) Multichannel Surface electromyography in ergonomics: potentialities and limits. Hum Factors Ergon Manuf Serv Ind 20(4):255–271CrossRefGoogle Scholar
  17. Heesakkers JPFA, Gerrestsen RRR (2004) Urinary incontinence: sphincter functioning from a urological perspective. Digestion 69(2):93–101CrossRefGoogle Scholar
  18. Holobar A, Farina D, Gazzoni M, Merletti R, Zazula D (2009) Estimating motor unit discharge patterns from high-density surface electromyogram. Clin Neurophysiol 120:551–562CrossRefGoogle Scholar
  19. Holobar A, Glaser V, Gallego JA, Dideriksen JL, Farina D (2012) Non-invasive characterization of motor unit behavior in pathological tremor. J Neural Eng 9(5):056011CrossRefGoogle Scholar
  20. Holobar A, Zazula D (2007) Multichannel blind source separation using Convolution Kernel Compensation. IEEE Tran Signal Proc 55:4487–4496MathSciNetCrossRefGoogle Scholar
  21. Keenan KG, Farina D, Maluf KS, Merletti R, Enoka RM (2005) Influence of amplitude cancellation on the simulated surface electromyogram. J Appl Physiol 98:120–131CrossRefGoogle Scholar
  22. Lapatki BG, Oostenveld R, Van Dijk JP, Jonas IE, Zwarts MJ, Stegeman DF (2006) Topographical characteristics of motor units of the lower facial musculature revealed by means of high-density surface EMG. J Neurophysiol 95:342–354CrossRefGoogle Scholar
  23. Lindstrom L, Magnusson R (1977) Interpretation of myoelectric power spectra: a model and its applications. Proc IEEE 65:653–662Google Scholar
  24. Masuda T, Miyano H, Sadoyama T (1985) The position of innervation zones in the biceps brachii investigated by surface electromyography. IEEE Trans Biomed Eng 32:36–42CrossRefGoogle Scholar
  25. Merletti R, Aventaggiato M, Botter A, Holobar A, Marateb H, Vieira TMM (2010) Advances in surface EMG: recent progress in detection and processing techniques. Crit Rev Biomed Eng 38:305–345CrossRefGoogle Scholar
  26. Merletti R, Botter A, Troiano A, Merlo E, Minetto MA (2009) Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art. Clin Biomech 24(2):122–134CrossRefGoogle Scholar
  27. Merletti R, Bottin A, Cescon C, Farina D, Gazzoni M, Martina S, Mesin L, Pozzo M, Rainoldi A, Enck P (2004) Multichannel surface EMG for the non-invasive assessment of the anal sphincter muscle. Digestion 69(2):112–122CrossRefGoogle Scholar
  28. Merletti R, Parker P (eds) (2004) Electromyography—physiology, engineering, and noninvasive applications. Wiley, HobokenGoogle Scholar
  29. Merletti R, Farina D (2009) Analysis of intramuscular electromyogram signals. Philos Transact A Math Phys Eng Sci 367(1887):357–368zbMATHCrossRefGoogle Scholar
  30. Merletti R, Farina D, Gazzoni M (2003) The linear electrode array: a useful tool with many applications. J Electromyogr Kinesiol 13:37–47CrossRefGoogle Scholar
  31. Merletti R, Holobar A, Farina D (2008) Analysis of motor units with high-density surface electromyography. J Electromyogr Kinesiol 18:879–890CrossRefGoogle Scholar
  32. Mesin L, Gazzoni M, Merletti R (2009) Automatic localisation of innervation zones: a simulation study of the external anal sphincter. J Electromyogr Kinesiol 19(6):e413–e421CrossRefGoogle Scholar
  33. Sherwood L (ed) (2008) Human physiology: from cells to systems. Human physiology. Brooks/Cole, Cengage Learning, BelmontGoogle Scholar
  34. Sjøgaard G, Søgaard K, Hermens HJ, Sandsjö L, Läubli T, Thorn S, Vollenbroek-Hutten MM, Sell L, Christensen H, Klipstein A, Kadefors R, Merletti R (2006) Neuromuscular assessment in elderly workers with and without work related shoulder/neck trouble: the NEW-study design and physiological findings. Eur J Appl Physiol 96(2):110–121CrossRefGoogle Scholar
  35. Soderberg GL (1992) Selected topics in surface electromyography for use in the occupational setting: expert perspectivesGoogle Scholar
  36. Stashuk DW, Farina D, Søgaard K (2004) Decomposition of intramuscular EMG signals. In: Merletti R and Parker PA (eds) Electromyography: Physiology, engineering, and noninvasive applications, Wiley-IEEE PressGoogle Scholar
  37. Vieira TMM, Loram I, Muceli S, Merletti R, Farina D (2011) Postural activation of the human gastrocnemius muscle: Are the motor units spatially localized? J Physiol 589:431–443CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Filipe Barroso
    • 1
    Email author
  • Diana Ruiz Bueno
    • 4
  • Juan Álvaro Gallego
    • 2
  • Paola Jaramillo
    • 3
  • Atilla Kilicarslan
    • 5
  1. 1.University of MinhoGuimarãesPortugal
  2. 2.Bioengineering GroupSpanish National Research Council (CSIC)MadridSpain
  3. 3.Mechanical EngineeringVirginia Polytechnic Institute and State UniversityVAUSA
  4. 4.Department of Computer Science and SystemsUniversity of ZaragozaZaragozaSpain
  5. 5.University of HoustonHoustonUSA

Personalised recommendations