当前位置: X-MOL 学术Int. J. Pattern Recognit. Artif. Intell. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Classification of Body Position During Muslim Prayer Using the Convolutional Neural Network
International Journal of Pattern Recognition and Artificial Intelligence ( IF 0.9 ) Pub Date : 2021-08-23 , DOI: 10.1142/s0218001421540288
Vahid Sobhani 1 , Koorosh Izadi 2 , Ehsan Manshadi Mokari 2 , Boshra Hatef 2
Affiliation  

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.

中文翻译:

使用卷积神经网络对穆斯林祈祷期间的身体姿势进行分类

背景:穆斯林祈祷(Namaz)是伊斯兰教中最重要的强制性宗教义务,每天由穆斯林在特定的规定时间定期进行五次。由于身体位置的变化会影响大脑活动,Namaz 可以被认为是评估身体位置快速变化对脑电图 (EEG) 测量的大脑活动影响的合适模型。方法:40 名穆斯林参与者在使用 14 通道脑电图记录仪记录他们的大脑活动时执行了一个四周期的 Namaz。在 Namaz 的不同位置(包括站立、鞠躬、跪拜和坐着)测量不同频带(delta、theta、alpha、beta 和 gamma)中的大脑连接(由本研究中 EEG 通道之间的相互关联定义)。结果:
更新日期:2021-08-23
down
wechat
bug