Signal, Image and Video Processing

, Volume 11, Issue 6, pp 1081–1088 | Cite as

Automatic analysis of human posture equilibrium using empirical mode decomposition

  • Khaled Safi
  • Samer Mohammed
  • Inke Marie Albertsen
  • Eric Delechelle
  • Yacine Amirat
  • Mohamad Khalil
  • Jean-Michel Gracies
  • Emilie Hutin
Original Paper


The present study proposes a new approach for the assessment of the human balance control. This approach is based on the decomposition of the center of pressure displacement using empirical mode decomposition (EMD) that provides an effective time-frequency analysis of non-stationary signals. Twenty-eight healthy subjects performed quiet standing in four conditions—feet apart/together with respect to eyes open/closed—while recording the stabilometric signals in the anteroposterior (AP) and mediolateral (ML) directions. The EMD method decomposes each stabilometric signal into several subsignals called intrinsic mode functions (IMFs). Stabilogram-diffusion analysis technique is applied to generate the diffusion curve of each IMF signal. Each diffusion curve is modeled as a second-order system and provides representative features, such as the gain parameter. Analysis of the gain parameter shows the major effect of visual input and feet conditions on the strategy to control/stabilize the balance. Significant differences were found between young and elderly, and between women and men. In addition, the impact of feet position seems to be higher in ML direction than in AP direction.


Balance analysis Posture Stabilometric signals Empirical mode decomposition 


  1. 1.
    Spoor, F., Garland, T., Krovitz, G., Ryan, T.M., Silcox, M.T., Walker, A.: The primate semicircular canal system and locomotion. Proc. Nat. Acad. Sci. 104(26), 10808–10812 (2007)CrossRefGoogle Scholar
  2. 2.
    Hertel, J., Olmsted-Kramer, L.C.: Deficits in time-to-boundary measures of postural control with chronic ankle instability. Gait Posture 25(1), 33–39 (2007)CrossRefGoogle Scholar
  3. 3.
    Horak, F.B.: Postural orientation and equilibrium: what do we need to know about neural control of balance to prevent falls? Age Ageing 35(suppl 2), ii7–ii11 (2006)MathSciNetGoogle Scholar
  4. 4.
    Chiari, L., Rocchi, L., Cappello, A.: Stabilometric parameters are affected by anthropometry and foot placement. Clin. Biomech. 17(9), 666–677 (2002)CrossRefGoogle Scholar
  5. 5.
    Redfern, M.S., Yardley, L., Bronstein, A.M.: Visual influences on balance. J. Anxiety Disord. 15(1), 81–94 (2001)CrossRefGoogle Scholar
  6. 6.
    Granacher, U., Bridenbaugh, S.A., Muehlbauer, T., AnjaWehrle, Kressig, R.W.: Age-related effects on postural control under multi-task conditions. Gerontology 57(3), 247–255 (2010)CrossRefGoogle Scholar
  7. 7.
    Callisaya, M.L., Blizzard, L., Schmidt, M.D., McGinley, J.L., Srikanth, V.K.: Ageing and gait variability—a population-based study of older people. Age Ageing 39, 191–197 (2010)CrossRefGoogle Scholar
  8. 8.
    Khasnis, A., Gokula, R.M., et al.: Rombergs test. J. Postgrad. Med. 49(2), 169 (2003)Google Scholar
  9. 9.
    Carneiro, J.A.O., Santos-Pontelli, T.E.G., Colafêmina, J.F., Carneiro, A.A.O., Ferriolli, E.: Analysis of static postural balance using a 3d electromagnetic system. Braz. J. Otorhinolaryngol. 76(6), 783–788 (2010)CrossRefGoogle Scholar
  10. 10.
    Winter, D.A.: Human balance and posture control during standing and walking. Gait Posture 3(4), 193–214 (1995)CrossRefGoogle Scholar
  11. 11.
    Gabell, A., Simons, M.A., Nayak, U.S.L.: Falls in the healthy elderly: predisposing causes. Ergonomics 28(7), 965–975 (1985)CrossRefGoogle Scholar
  12. 12.
    Prudham, D., Grimley Evans, J.: Factors associated with falls in the elderly: a community study. Age Ageing 10(3), 141–146 (1981)CrossRefGoogle Scholar
  13. 13.
    Zok, M., Mazza, C., Cappozzo, A.: Should the instructions issued to the subject in traditional static posturography be standardised? Med. Eng. Phys. 30(7), 913–916 (2008)CrossRefGoogle Scholar
  14. 14.
    McIlroy, W.E., Maki, B.E.: Preferred placement of the feet during quiet stance: development of a standardized foot placement for balance testing. Clin. Biomech. 12(1), 66–70 (1997)CrossRefGoogle Scholar
  15. 15.
    Carpenter, M.G., Frank, J.S., Winter, D.A., Peysar, G.W.: Sampling duration effects on centre of pressure summary measures. Gait Posture 13(1), 35–40 (2001)CrossRefGoogle Scholar
  16. 16.
    Raymakers, J.A., Samson, M.M., Verhaar, H.J.J.: The assessment of body sway and the choice of the stability parameter(s). Gait Posture 21(1), 48–58 (2005)CrossRefGoogle Scholar
  17. 17.
    Prieto, T.E., Myklebust, J.B., Hoffmann, R.G., Lovett, E.G., Myklebust, B.M.: Measures of postural steadiness: differences between healthy young and elderly adults. IEEE Trans. Biomed. Eng. 43(9), 956–966 (1996)CrossRefGoogle Scholar
  18. 18.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903–995. The Royal Society, London (1998)Google Scholar
  19. 19.
    Niang, O., Delechelle, E., Lemoine, J.: A spectral approach for sifting process in empirical mode decomposition. IEEE Trans. Signal Process. 58(11), 5612–5623 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Nunes, J.-C., Delechelle, E.: Empirical mode decomposition: applications on signal and image processing. Adv. Adapt. Data Anal. 1(01), 125–175 (2009)CrossRefGoogle Scholar
  21. 21.
    Liang, H., Lin, Q.-H., Chen, J.D.Z.: Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease. IEEE Trans. Biomed. Eng. 52(10), 1692–1701 (2005)CrossRefGoogle Scholar
  22. 22.
    Blanco-Velasco, M., Weng, B., Barner, K.E.: Ecg signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 38(1), 1–13 (2008)CrossRefGoogle Scholar
  23. 23.
    Li, S., Zhou, W., Yuan, Q., Geng, S., Cai, D.: Feature extraction and recognition of ictal EEG using EMD and SVM. Comput. Biol. Med. 43(7), 807–816 (2013)CrossRefGoogle Scholar
  24. 24.
    Guo, J., Qin, S., Zhu, C.: The application of energy operator demodulation approach based on emd in mechanical system identification. In 2012 19th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 80–85. IEEE (2012)Google Scholar
  25. 25.
    Meng, H., Liang, H.: Search for information-bearing components in neural data. PLoS ONE 9(6), e99793 (2014)CrossRefGoogle Scholar
  26. 26.
    Meng, H., Liang, H.: Intrinsic mode entropy based on multivariate empirical mode decomposition and its application to neural data analysis. Cogn. Neurodyn. 5(3), 277–284 (2011)CrossRefGoogle Scholar
  27. 27.
    Liang, H., Bressler, S.L., Desimone, R., Fries, P.: Empirical mode decomposition: a method for analyzing neural data. Neurocomputing 65, 801–807 (2005)CrossRefGoogle Scholar
  28. 28.
    Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(01), 1–41 (2009)CrossRefGoogle Scholar
  29. 29.
    Enriquez, N.: A simple construction of the fractional brownian motion. Stoch. Process. Appl. 109(2), 203–223 (2004)MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Mandelbrot, B.B., Van Ness, J.W.: Fractional brownian motions, fractional noises and applications. SIAM Rev. 10(4), 422–437 (1968)MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Collins, J.J., De Luca, C.J.: Open-loop and closed-loop control of posture: a random-walk analysis of center-of-pressure trajectories. Exp. Brain Res. 95(2), 308–318 (1993)CrossRefGoogle Scholar
  32. 32.
    Collins, J.J., De Luca, C.J.: Random walking during quiet standing. Phys. Rev. Lett. 73(5), 764 (1994)CrossRefGoogle Scholar
  33. 33.
    James, J., Collins, J.J., De Luca, C.J.: The effects of visual input on open-loop and closed-loop postural control mechanisms. Exp. Brain Res. 103(1), 151–163 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • Khaled Safi
    • 1
    • 2
  • Samer Mohammed
    • 1
  • Inke Marie Albertsen
    • 3
  • Eric Delechelle
    • 1
  • Yacine Amirat
    • 1
  • Mohamad Khalil
    • 2
    • 4
  • Jean-Michel Gracies
    • 3
  • Emilie Hutin
    • 3
  1. 1.Laboratoire LISSIUniversité Paris-Est Créteil (UPEC)Vitry-sur-SeineFrance
  2. 2.Centre AZM pour la recherche en biotechnologie, EDST, Université LibanaiseBeirutLebanon
  3. 3.Laboratoire ARM, EA 7377 BIOTN, UPEC, Service de Rééducation NeurolocomotriceCHU Henri MondorCréteilFrance
  4. 4.Laboratoire CRSI, Faculté de génie, Université LibanaiseBeirutLebanon

Personalised recommendations