Complex feature analysis of center of pressure signal for age-related subject classification

omid khayat; Fereidoun  Nowshiravan-Rahatabad

Annals of Military and Health Sciences Research

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Complex feature analysis of center of pressure signal for age-related subject classification

Author(s):

omid khayat^1,*,

Fereidoun Nowshiravan-Rahatabad²

1Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran, Andorra

2Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran, Andorra

Annals of Military and Health Sciences Research:Vol. 12, issue 1; e63518

Published online:Jan 23, 2014

Article type:Research Article

Received:Sep 24, 2013

Accepted:Dec 26, 2013

How to Cite:omid khayatFereidoun Nowshiravan-RahatabadComplex feature analysis of center of pressure signal for age-related subject classification.Ann Mil Health Sci Res.12(1):e63518.

Abstract

Materials and Methods: The elderly individuals’ behavior during standing and how demanding such a task is for them, is still unknown. We recorded the center of pressure (COP) position of 12 elder and 15 young participants while they were standing for 30 seconds. Then an analysis was performed to find the most appropriate and discriminative features for the elderly and young posture signals discrimination. Features were selected in frequency and time domain. Largest Lyapunov exponents of the COP signals were also computed to show the impact of chaotic behavior in static balance characterization relative to age.

Results: Working in frequency domain is preferred to time domain analysis and largest Lyapunov exponent of the posture signal can be representatively used for COP signal discrimination between the two classes of subjects.

Conclusion: In investigation and analysis of static balance for elders and unhealthy participants the signal of COP can be studied in chaotic domain beside frequency domain. Extraction of features from both chaotic and frequency domains significantly improves the discrimination rate of balance signals in age-related classes.

Keywords

static balance

center of pressure

age relation

largest Lyapunov exponent

feature extraction

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