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All Journal Journal of ICT, Design, Engineering and Technological Science
Fahad Shah Bukhari
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Utilizing a Hybrid Deep Learning Architecture For Salat Posture Detection Abdul Salam Shah; Farhan Akbar; Muhammad Adnan Kaim Khani; Adil Maqsood; Fahad Shah Bukhari
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.1.3

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.
Co-Authors Abdul Salam Shah Adil Maqsood Farhan Akbar Muhammad Adnan Kaim Khani