title: Muscles data compression in body sensor network using the principal component analysis in wavelet domain.
abstract: BACKGROUND
  Body sensor network is a key technology that is used for supervising the physiological information from a long distance that enables physicians to predict and diagnose effectively the different conditions. 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 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 showed 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 ratios 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 ratios in previous method (Tseng algorithm).
  
  
  CONCLUSIONS
  Comparing compression rates and prediction errors with the available results show the exactness of the proposed method.
authors.affiliation: Faculty of Computer, College of Engineering, East Azerbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran.
authors.forename: Elmira
authors.initials: E
authors.lastname: Yekani Khoei
authors.affiliation: Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran.
authors.forename: Reza
authors.initials: R
authors.lastname: Hassannejad
authors.affiliation: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
authors.forename: Behzad
authors.initials: B
authors.lastname: Mozaffari Tazehkand
keywords: Body sensor network; Data compression; Prediction; Principal component analysis; Wavelet transform