• Analysis of center of pressure signals using empirical mode decomposition and Fourier-Bessel expansion

      Pachori, Ram Bilas; Hewson, David; Snoussi, Hichem; Duchêne, Jacques (IEEE, 2008-12-31)
      Center of pressure (COP) measurements are often used to identify balance problems. A new method for analysis of COP signals using empirical mode decomposition (EMD) and Fourier-Bessel (FB) expansion is proposed in this paper. The EMD decomposes a COP signal into a finite set of band-limited signals termed intrinsic mode functions (IMFs), before FB expansion is applied on each IMF to compute mean frequency. The FB expansion based representation is suitable for use in non-stationary and very short duration signals. Seventeen subjects were tested under eyes open (EO) and eyes closed (EC) conditions, with different vibration frequencies applied for EC condition to further perturb sensory information. Mean frequency as calculated by FB expansion for the first three IMFs was able to distinguish between EO and EC conditions (p < 0.05), while only first IMF was able to detect a vibration effect.
    • Postural time-series analysis using Empirical Mode Decomposition and second-order difference plots

      Pachori, Ram Bilas; Hewson, David; Snoussi, Hichem; Duchêne, Jacques (IEEE, 2009-05-26)
      This paper presents a new method for analysis of center of pressure (COP) signals using empirical mode decomposition (EMD). The EMD decomposes a COP signal into a finite set of band-limited signals termed as intrinsic mode functions (IMFs). Thereafter, a signal processing technique used in continuous chaotic modeling is used to investigate the difference between experimental conditions on the summed IMFs. This method is used to detect the degree of variability from a second-order difference plot, which is quantified using a Central Tendency Measure (CTM). Seventeen subjects were tested under eyes open (EO) and eyes closed (EC) conditions, with different vibration frequencies applied for the EC condition in order to provide additional sensory perturbation. This study has demonstrated an effective way to differentiate vibration frequencies by combining EMD and second-order difference (SOD) plots.