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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: In the wavelet transform block ( Figs. 4 and 5) removing the noise from received data of the sensors are eliminated. Since these data have noise and interference, in the proposed method Dabuchi 4 wavelet transform is used for removing the noise, signal compression and reduction of the interferences. Dabuchi 4 wavelet is suitable for multi resolution analysis of wavelet; moreover orthogonality is the other advantage of this wavelet. Some wavelets are without orthogonal property and there are problems of signal energy reduction and frequency leakage. Transformed PXi(q) data in previous block is given as entering signals to Dabuchi 4 wavelet transform, then wavelet transform decomposes base signal to two signal levels according to high-pass and low-pass filters like Fig. 7. As, it is shown in Fig. 7, high-pass signal is the noisy signal that is put aside. Low-pass signals in every dimension of each sensor are saved in WXi(1:m) for each i=1,2,…n. The remaining process continues over the low-pass signal whose noises are reduced.


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: In the wavelet transform block ( Figs. 4 and 5) removing the noise from received data of the sensors are eliminated. Since these data have noise and interference, in the proposed method Dabuchi 4 wavelet transform is used for removing the noise, signal compression and reduction of the interferences. Dabuchi 4 wavelet is suitable for multi resolution analysis of wavelet; moreover orthogonality is the other advantage of this wavelet. Some wavelets are without orthogonal property and there are problems of signal energy reduction and frequency leakage. Transformed PXi(q) data in previous block is given as entering signals to Dabuchi 4 wavelet transform, then wavelet transform decomposes base signal to two signal levels according to high-pass and low-pass filters like Fig. 7. As, it is shown in Fig. 7, high-pass signal is the noisy signal that is put aside. Low-pass signals in every dimension of each sensor are saved in WXi(1:m) for each i=1,2,…n. The remaining process continues over the low-pass signal whose noises are reduced.

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