Postural time-series analysis using Empirical Mode Decomposition and second-order difference plots - Archive ouverte HAL Access content directly
Conference Papers Year :

Postural time-series analysis using Empirical Mode Decomposition and second-order difference plots

(1) , (2) , (2) , (2)
1
2

Abstract

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.

Dates and versions

hal-02358736 , version 1 (12-11-2019)

Identifiers

Cite

Ram Bilas Pachori, David Hewson, Hichem Snoussi, Jacques Duchene. Postural time-series analysis using Empirical Mode Decomposition and second-order difference plots. ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2009, Taipei, Taiwan. pp.537-540, ⟨10.1109/ICASSP.2009.4959639⟩. ⟨hal-02358736⟩
14 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More