(2021) Classification of Body Position During Muslim Prayer Using the Convolutional Neural Network. International Journal of Pattern Recognition and Artificial Intelligence. p. 13. ISSN 0218-0014
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Abstract
Background: Muslim prayer (Namaz) is the most important obligatory religious duty in Islam that is regularly performed five times per day at specific prescribed times by Muslims. Due to the fact that change of body position affects brain activity, Namaz can be considered as a suitable model to assess the effect of quick changes of the body position on brain activity measured by electroencephalography (EEG). Methods: Forty Muslim participants performed a four-cycle Namaz while their brain activity was being recorded using a 14-channel EEG recorder. The brain connectivity (as defined by a mutual correlation between EEG channels in this study) in different frequency bands (delta, theta, alpha, beta, and gamma) was measured in various positions of Namaz including standing, bowing, prostration, and sitting. Results: The results indicated that the delta band demonstrates the most changes in cross-correlation between the recorded channels, and finally, the accuracy of 73.8 was obtained in the data classification.
Item Type: | Article |
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Keywords: | Body position Namaz convolutional neural network cross-correlation images Computer Science |
Page Range: | p. 13 |
Journal or Publication Title: | International Journal of Pattern Recognition and Artificial Intelligence |
Journal Index: | ISI |
Volume: | 35 |
Number: | 11 |
Identification Number: | https://doi.org/10.1142/s0218001421540288 |
ISSN: | 0218-0014 |
Depositing User: | مهندس مهدی شریفی |
URI: | http://eprints.bmsu.ac.ir/id/eprint/9549 |
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