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

AUTHORS

omid khayat 1 , * , Fereidoun Nowshiravan-Rahatabad 2

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

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

How to Cite: khayat O, Nowshiravan-Rahatabad F. Complex feature analysis of center of pressure signal for age-related subject classification, Ann Mil Health Sci Res. 2014 ; 12(1):e63518.

ARTICLE INFORMATION

Annals of Military and Health Sciences Research: 12 (1); e63518
Published Online: January 23, 2014
Article Type: Original Article
Received: September 24, 2013
Accepted: December 26, 2013

Crossmark

CHEKING

READ FULL TEXT
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

© 2014, Annals of Military and Health Sciences Research. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

Fulltext

Full text is available in PDF.

References

  • 1.

    references is available in PDF.

  • COMMENTS

    LEAVE A COMMENT HERE: