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, 2020Abstract:
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.
Anis Koubâa
Adel AmmarEvents:
Document:
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Download6th International Conference on Data Science and Machine Learning Applications (CDMA 2020), pp 106-111.
Riyadh, Saudi Arabia.
DOI:10.1109/CDMA47397.2020.00024.
ISBN: 978-1-7281-2746-0.
Record Date: 14, Jan, 2020