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Bioimpacts. 2015;5(1): 9-16.
doi: 10.15171/bi.2015.03
PMID: 25901292
PMCID: PMC4401169
Scopus ID: 84924901521
  Abstract View: 1560
  PDF Download: 957

Original Research

Muscles Data Compression in Body Sensor Network using the Principal Component Analysis in Wavelet Domain

Elmira Yekani Khoei 1, Reza Hassannejad 2*, Behzad Mozaffari Tazehkand 3

1 Faculty of Computer, College of Engineering, East Azerbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran
2 Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
3 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
*Corresponding Author: Email: hassannejhad@tabrizu.ac.ir

Abstract

 Introduction: Body sensor network is a key technology that is used for supervising the physiological information from long distance that enables physicians to predict, diagnose effectively the different conditions from long distance. These networks include small sensors with the ability of sensing where there are some limitations in calculating and energy.
Methods: In the present research, a new compression method based on the analysis of principal components and wavelet transform is used to increase the coherence. In the present method, the first analysis of the main principles is used to find the principal components of the data in order to increase the coherence for increasing the similarity between the data and compression rate. Then, according to the ability of wavelet transform, data are decomposed to different scales. In restoration process of data only special parts are restored and some parts of the data that include noise are omitted. By noise omission, the quality of the sent data increases and good compression could be obtained.
 Results: Pilates practices were executed among twelve patients with various dysfunctions. The results show 0.7210, 0.8898, 0.6548, 0.6765, 0.6009, 0.7435, 0.7651, 0.7623, 0.7736, 0.8596, 0.8856 and 0.7102 compression ratio in proposed method and 0.8256, 0.9315, 0.9340, 0.9509, 0.8998, 0.9556, 0.9732, 0.9580, 0.8046, 0.9448, 0.9573 and 0.9440 compression ratio for previous method (Tseng algorithm).
Conclusion: Comparing compression rate and prediction errors with the available results shows the exactness of the proposed method.
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Submitted: 01 Nov 2014
ePublished: 15 Aug 2017
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