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Muscles data compression in body sensor network using the principal component analysis in wavelet domain.

Yekani Khoei E, Hassannejad R, Mozaffari Tazehkand B - Bioimpacts (2015)

Bottom Line: 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.Pilates practices were executed among twelve patients with various dysfunctions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Computer, College of Engineering, East Azerbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran.

ABSTRACT

Introduction: 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).

Conclusion: Comparing compression rates and prediction errors with the available results show the exactness of the proposed method.

No MeSH data available.


Related in: MedlinePlus

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Mentions: Online phase: the main aim of this phase is data compression according to produced compression tree in offline stage. There are two cases for each sensor. 1. Vi node which is the first level sensor node. 2. Vi node which is not first level sensor node. The stages shown in ( Fig. 2) and ( Fig. 3) are performed for case 1 and 2.


Muscles data compression in body sensor network using the principal component analysis in wavelet domain.

Yekani Khoei E, Hassannejad R, Mozaffari Tazehkand B - Bioimpacts (2015)

© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401169&req=5

Mentions: Online phase: the main aim of this phase is data compression according to produced compression tree in offline stage. There are two cases for each sensor. 1. Vi node which is the first level sensor node. 2. Vi node which is not first level sensor node. The stages shown in ( Fig. 2) and ( Fig. 3) are performed for case 1 and 2.

Bottom Line: 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.Pilates practices were executed among twelve patients with various dysfunctions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Computer, College of Engineering, East Azerbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran.

ABSTRACT

Introduction: 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).

Conclusion: Comparing compression rates and prediction errors with the available results show the exactness of the proposed method.

No MeSH data available.


Related in: MedlinePlus