Advertisement

Sensitivity Analysis of HD-sEMG Amplitude Descriptors Relative to Grid Parameter Variation

  • Vincent CarriouEmail author
  • Mariam Al Harrach
  • Jeremy Laforet
  • Sofiane Boudaoud
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 57)

Abstract

The aim of this work is to perform a sensitivity analysis of a high density surface electromyogram (HD-sEMG) amplitude descriptors according to several grid parameters. For this purpose, an analytical limb model is used, where the upper limb is modeled as a multilayered cylinder with three layers: muscle, fat tissue and skin tissue. Using this model, HD- sEMG signals are computed over the skin as a 2D surface along angular and longitudinal directions. Electrode recording is performed through a surface integration on the 2D surface according to the electrode shape. 3 simulations with the same anatomy (350 Motor Units) were computed for 3 constant contraction levels: 30%, 50% and 70% of the Maximal Voluntary Contraction (MVC). Then, a global sensitivity analysis using Morris formalism is performed to explore the sensitivity of amplitude descriptors (ARV, RMS and HOS) relative to vary parameters from the electrode grid (inter-electrode distances, electrodes radius, position and rotation). The obtained results clearly exposed a huge impact of the grid rotation on the studied criteria. They also showed that parameters specific to the electrode grid layout (inter-electrode distances) have the less impact.

Keywords

Sensitivity analysis Amplitude descriptors HD- sEMG modeling Morris method 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D. Farina, L. Mesin, S. Martina, R. Merletti (2004) A surface EMG generation model with multilayer cylindrical description of the volume conductor, IEEE Transactions on BioMedical Engineering, 51 (3): 415–426Google Scholar
  2. 2.
    D. Farina, C. Cescon, R. Merletti (2002) Influence of anatomical, physical, and detection-system parameters on surface EMG, Biological Cybernetics, 86:445-456Google Scholar
  3. 3.
    T. Heidlauf, O. Röhrle (2013) Modeling of Chemoelectromechanical behvaior of skeletal muscle using the parallel open-source software library OpenCMISS, Computational and Mathematical Methods in MedicineGoogle Scholar
  4. 4.
    R. Merletti, P. A. Parker, Electromyography: Physiology, Engineering, and Non-Invasive Applications, 2004Google Scholar
  5. 5.
    E. Clancy and N. Hogan (1999) Probability density of the surface electromyogram and its relation to amplitude detectors, IEEE Transactions on Biomedical Engineering, 46 (6): 730–739Google Scholar
  6. 6.
    F. Ayachi, S. Boudaoud, J. Grosset, and C. Marque (2011) Study of the muscular force/hos parameters relationship from the surface eletromyogram, in 15th NBC on Biomedical Engineering & Medical Physics, Aalborg, Denmark, vol. 34, 2011, pp 187–190Google Scholar
  7. 7.
    M. D. Morris (1991) Factorial sampling plans for preliminary computational experiments, Technometrics, 33 (2): 161-174Google Scholar
  8. 8.
    J. Laforet, C. Marque (2013) Preliminary global sensitivity analysis of a uterine electrical activity model, in 35th Annual International Conference of the IEEE EMBC, Osaka, Japan, 2013, pp 7440-7443Google Scholar
  9. 9.
    F. S. Ayachi, S. Boudaoud, C. Marque (2014) Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study, Medical & Biological Engineering & Computing, 52 (8): 673–684Google Scholar
  10. 10.
    T. I. Arabadzhiev, V. G. Dimitrov, N. A. Dimitrova, G. V. Dimitrov (2010) Influence of motor unit synchronization on amplitude characteristics of surface and intramuscularly recorded EMG signals, European Journal of Applied Physiology, 108 (2): 227–237Google Scholar
  11. 11.
    A. Saltelli, F. Campolongo, J. Cariboni (2009) Screening important inputs in models with strong interaction properties, Reliability Engineering and System Safety, 94: 1149-1155Google Scholar
  12. 12.
    M. Al Harrach, S. Boudaoud, D. Gamet, J. F. Grosset, F. Marin (2014) Evaluation of HD-sEMG Probability Density Function Deformations in Ramp Exercise, in 36th Annual International Conference of the IEEE EMBC, Chicago, USA, 2014, pp 2209-2212Google Scholar
  13. 13.
    S. Boudaoud, S. Allouch, M. Al Harrach, F. Marin (2015) On the benefits of using HD-sEMG technique for estimating muscle force, Computer Methods in Biomechanics and Biomedical Engineering, 18: 1890-1891.Google Scholar
  14. 14.
    E. M. Maathuis, J. Drenthen, J. P. van Dijk, G. H. Visser, J. H. Blok (2011) Motor unit tracking with high-density surface EMG, Journal of Electromyography and Kinesiology, 18: 920-930.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Vincent Carriou
    • 1
    Email author
  • Mariam Al Harrach
    • 1
  • Jeremy Laforet
    • 1
  • Sofiane Boudaoud
    • 1
  1. 1.Sorbonne University, Universite de Technologie de Compiègne, CNRS UMR 7338 Biomechanics and Bioengineering, Centre de Recherche de RoyallieuCompiègneFrance

Personalised recommendations