Citation

Al-Selwi, Hatem Fahd and Abd. Aziz, Azlan and Abas, Fazly Salleh and Abdul Razak, Siti Fatimah and Amir Hamzah, Nur Asyiqin (2022) Neural Networks Based Prayer Monitoring and Recognition Framework. In: 2022 International Conference on Digital Transformation and Intelligence (ICDI), 1-2 Dec. 2022, Sarawak, Malaysia.

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Abstract

Daily prayer is an important part of every muslim around the globe and muslims makes up 24.7% of the world population. The rapid advancement in smartphones and wearable devices sensors', enabled research to utilize these sensors for more complex human activity recognition tasks. In this paper, we propose a framework that leverages smartwatch sensors for prayer monitoring and recognition. Our work is mainly targeting smartwatch due their convenience for the users, but challenging nature of unlimited movement and noise during prayer. Our framework allows user to store their prayer using a smartwatch with the help of our web applications. We further propose a deep learning-based timeseries data classification model for prayer recognition. In this study, we have used three sensors namely heartrate electrocardiogram, an accelerometer, and a gyroscope. The collected data is used to train a prayer classification model. The raw prayer data is pre-processed using moving average, data aggregation, and data segmentation. Our proposed prayer classification model showed an accuracy of 87%. Finally, we discussed a suggested future work and further research in prayer analysis area.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Activity Recognition , Muslim Prayer , Multiple Sensors , Smartwatch , Wearable Devices
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	15 Mar 2023 04:06
Last Modified:	15 Mar 2023 04:06
URII:	http://shdl.mmu.edu.my/id/eprint/11228

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