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

2020Conference object Restricted access
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dc.contributor.authorKoubaa, Anis
dc.contributor.authorAmmar, Adel
dc.contributor.authorBenjdira, Bilel
dc.contributor.authorAl Hadid, Abdullatif
dc.contributor.authorKawaf, Belal
dc.contributor.authorAl Yahri, Saleh Ali
dc.contributor.authorBabiker, Abdelrahman
dc.contributor.authorAssaf, Koutaiba
dc.contributor.authorBa Ras, Mohannad
dc.date.accessioned2020-10-21T11:37:37Z
dc.date.embargo2120
dc.date.issued2020
dc.description.abstractIn 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/CDMA47397.2020.00024pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/16347
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversion978-1-7281-2746-0pt_PT
dc.subjectDeep learningpt_PT
dc.subjectActivity recognitionpt_PT
dc.subjectTrainingpt_PT
dc.subjectCameraspt_PT
dc.subjectTask analysispt_PT
dc.titleActivity Monitoring of Islamic Prayer (Salat) Postures using Deep Learningpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceRiyadh, Saudi Arabiapt_PT
oaire.citation.endPage111pt_PT
oaire.citation.startPage106pt_PT
oaire.citation.titleProceedings of the 6th International Conference on Data Science and Machine Learning Applications (CDMA 2020)pt_PT
person.familyNameKoubaa
person.givenNameAnis
person.identifier989131
person.identifier.ciencia-idCA19-2399-D94A
person.identifier.orcid0000-0003-3787-7423
person.identifier.scopus-author-id15923354900
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication0337d7df-5f77-46a4-8269-83d14bd5ea6b
relation.isAuthorOfPublication.latestForDiscovery0337d7df-5f77-46a4-8269-83d14bd5ea6b

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