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Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning
Ref: CISTER-TR-200108       Publication Date: 4 to 5, Mar, 2020

Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

Ref: CISTER-TR-200108       Publication Date: 4 to 5, Mar, 2020

Abstract:
In the Muslim community, the prayer (i.e. Salat) is the second pillar of Islam, and it is the most essential and fundamental worshiping activity that believers have to perform five times a day. From a gestures’ perspective, there are predefined human postures that must be performed in a precise manner. However, for several people, these postures are not correctly performed, due to being new to Salat or even having learned prayers in an incorrect manner. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an artificial intelligence assistive framework that guides worshippers to evaluate the correctness of the postures of their prayers. This paper represents the first step to achieve this objective and addresses the problem of the recognition of the basic gestures of Islamic prayer using Convolutional Neural Networks (CNN). The contribution of this paper lies in building a dataset for the basic Salat positions, and train a YOLOv3 neural network for the recognition of the gestures. Experimental results demonstrate that the mean average precision attains 85% for a training dataset of 764 images of the different postures. To the best of our knowledge, this is the first work that addresses human activity recognition of Salat using deep learning.

Authors:
Anis Koubâa
,
Adel Ammar
,
Bilel Benjdira
,
Abdullatif Al Hadid
,
Belal Kawaf
,
Saleh Ali Al Yahri
,
Abdelrahman Babiker
,
Koutaiba Assaf
,
Mohannad Ba Ras


Events:

CDMA 2020
4, Mar, 2020 >> 5, Mar, 2020
6th International Conference On Data Science And Machine Learning Applications
Riyadh, Saudi Arabia


6th International Conference on Data Science and Machine Learning Applications (CDMA 2020).
Riyadh, Saudi Arabia.



Record Date: 14, Jan, 2020