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

TL;DR

This paper introduces a deep learning-based system to recognize Islamic prayer postures, utilizing a new dataset and CNN models, achieving 85% accuracy, aiming to assist worshippers in performing Salat correctly.

Contribution

It is the first to develop a dataset and apply CNNs for recognizing Salat postures, advancing AI-assisted worship guidance.

Findings

Achieved 85% mean average precision in gesture recognition

Built a new dataset with 764 images of prayer postures

First application of deep learning for Salat activity recognition

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.