Utilizing a Hybrid Deep Learning Architecture For Salat Posture Detection

Authors

  • Abdul Salam Shah School of Computer Science, Faculty of Innovation and Technology, Taylor’s University, Subang Jaya, Selangor, Malaysia
  • Farhan Akbar Department of Computer Science, ILMA University, Karachi, Pakistan
  • Muhammad Adnan Kaim Khani Department of Computer Science, ILMA University, Karachi, Pakistan
  • Adil Maqsood Visionerz Pvt LTD,Karachi, Pakistan
  • Fahad Shah Bukhari Malaysian Institute of Information Technology, University of Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.33150/JITDETS-9.1.3

Keywords:

Real‑time human detection, IOT, Salat / Salah, Posture recognition, Innovation in religious practice, Convolutional Neural Networks (CNNs)

Abstract

A lot of Muslims have trouble getting their daily prayers right. You know, Salat with the movements and the recitations. It disrupts their religious duties. They do not get quick feedback on how their form looks. So we put together this system. It grabs images right as they happen. Then it checks them out using a convolutional neural network. That is CNN for short. It spots and confirms the basic postures in Salat. The thing covers six main positions. Takbir. Qiyam. Ruku. Sujood. Tashahhud. And Salam. Pretty much opens it up for tons of people to use. We tested how well it works. Looked at pose detection accuracy. Response time, too. And what users thought about it. Turns out the system helps a bunch. Folks can improve their Salat quality with it. Shows how computer vision and deep learning fit into something like this. Not your usual setup.

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Published

2025-06-23

How to Cite

[1]
Abdul Salam Shah, Farhan Akbar, Muhammad Adnan Kaim Khani, Adil Maqsood, and Fahad Shah Bukhari, “Utilizing a Hybrid Deep Learning Architecture For Salat Posture Detection”, J. ICT des. eng. technol. sci., vol. 9, no. 1, p. 16‑22, Jun. 2025.

Issue

Volume 9, Issue 1

Section

Articles