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
  • 84 Downloads

Abstract

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.

Keywords

Balance analysis Posture Stabilometric signals Empirical mode decomposition 

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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

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