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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: Tseng et al10 represented a method for data compression using temporal and spatial correlation in 2011 in which temporal and spatial correlation was used to optimum use of band width of connective wireless channel restrictions and reduction of conflict among the sensors transfer.10 In the present research, a network of body wireless sensor network with n sensor nodes has been considered in which every sensor node is equipped with m axis. Considering that in the present research, tri-axial accelerometers have been used, m is regarded 3. The mentioned algorithm has two offline and online phases. In offline phase, received data from the sensors in body sensor network are collected and sent for processing to a fusion center analyser. The main aim of the present phase is to achieve suitable order of the sensor node prediction. Block diagram of Fig. 1 shows the details of offline phase.


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: Tseng et al10 represented a method for data compression using temporal and spatial correlation in 2011 in which temporal and spatial correlation was used to optimum use of band width of connective wireless channel restrictions and reduction of conflict among the sensors transfer.10 In the present research, a network of body wireless sensor network with n sensor nodes has been considered in which every sensor node is equipped with m axis. Considering that in the present research, tri-axial accelerometers have been used, m is regarded 3. The mentioned algorithm has two offline and online phases. In offline phase, received data from the sensors in body sensor network are collected and sent for processing to a fusion center analyser. The main aim of the present phase is to achieve suitable order of the sensor node prediction. Block diagram of Fig. 1 shows the details of offline phase.

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