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ISSN: 2376-0281

International Journal of Neurorehabilitation
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Research Article

Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis

Noriko Tanaka1, Nor Aini Zakaria2,3, Nelson Kipchirchir Kibinge2, Shigehiko Kanaya2, Toshiyo, Tamura1*, and Masaki Yoshida1

1Department of Physical Therapy, School of Biomedical Engineering, Osaka Electro Communication University, Japan

2School of Information Science, Nara Institute of Science and Technology, Japan

3Department of Biomedical Instrumentation and Electronics, University Teknologi Malaysia, Johor, Malaysia

*Corresponding Author:
Toshiyo Tamura
Department of Physical Therapy
School of Biomedical Engineering
Osaka Electro Communication University, Japan
E-mail: [email protected]

Received date: February 28, 2014; Accepted date: April 28, 2014; Published date: May 30, 2014

Citation: Tanaka N, Zakaria NA, Kibinge NK, Kanaya S, Tamura T, et al. (2014) Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis. Int J Neurorehabilitation 1:106. doi:10.4172/2376-0281.1000106

Copyright: © 2014 Tanaka N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

This study aimed to show that important fall risk measures among the elderly can be classified using multiple parameters obtained from wearable inertial sensors. The timed up-and-go (TUG) test, a well-known standard assessment test, was used to evaluate the risk of falling among elderly individuals. The use of wearable inertial sensors enables extraction of triaxial acceleration and angular velocity signals for offline analysis. Thirty-eight elderly patients from Fujimoto Hayasuzu Hospital participated in this study. Specific results were provided from the signals obtained from acceleration and angular velocity, and analysis was carried out in each phase of various activities, such as sit-to-stand, walking, etc. Seventy-eight parameters were obtained from the extracted acceleration and angular velocity signals in all phases to classify the risk of falling among the elderly. Using principle component analysis, the most important measures were selected from the gathered parameters. The most influential measure in differentiating subjects with high and low fall risks was the turning angular velocity signal.

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