Real Time Pain Detection Using Facial Action Units in Telehealth System

During the Covid-19 pandemic, to reduce staff exposure to ill people, minimize the impact of patient surges on facilities, and preserve personal protective equipment, the recommendations are made by the World Health Organization to change the way that health care is delivered. Several telehe...

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Autor principal:	dalya abdullah anwar
Formato:	article
Lenguaje:	EN
Publicado:	Salahaddin University-Erbil 2021
Materias:	pain assessment face expression aam svm covid-19 telehealth. Technology T Science Q
Acceso en línea:	https://doaj.org/article/04b5ddb983a943209f8abf8c2a835226
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spelling	oai:doaj.org-article:04b5ddb983a943209f8abf8c2a8352262021-11-07T06:05:09ZReal Time Pain Detection Using Facial Action Units in Telehealth System10.21271/ZJPAS.33.5.42218-02302412-3986https://doaj.org/article/04b5ddb983a943209f8abf8c2a8352262021-10-01T00:00:00Zhttps://zancojournals.su.edu.krd/index.php/JPAS/article/view/4092https://doaj.org/toc/2218-0230https://doaj.org/toc/2412-3986 During the Covid-19 pandemic, to reduce staff exposure to ill people, minimize the impact of patient surges on facilities, and preserve personal protective equipment, the recommendations are made by the World Health Organization to change the way that health care is delivered. Several telehealth systems are utilized including live audio-video interaction or real-time telephone typically with a patient using a computer, smartphone, or tablet. During these appointments, the doctors need to know the pain levels of the patient to be able to prescribe the correct medicine and diagnose the disease proficiently. In this paper, a real-time 4- pain levels recognition based on facial expression during telehealth is proposed. Generally, the pain is measured via verbal communication, normally the patient’s self-report. However, if the patient has a disability and unable to communicate with others due to being impaired mentally or having breathing problems or the child self-reporting may not be a perfect way to measure the pain. The proposed system consists of two methods to detect pain from a patient’s facial expressions. The AAM_Based method detects the face and facial landmarks from each video frame using Active Appearance Model AAM, these landmarks are used to compute the facial features. The AU_Based method uses Facial Action Units AU which objectively describes facial muscle activations that are considered as Region of Interest. Support Vector Machine classifier is utilized to detect the levels of pain. A labeled dataset such as Biovid is used to train test, and the AAM_based method, while and UNBC is used for the second method. The findings show that it is possible to depend on facial expression to detect pain level 1 and level 4 very accurately, while it is very tricky to detect pain level 2, and 3 because the AUs for them are similar for most of the patients.dalya abdullah anwarSalahaddin University-Erbilarticlepain assessmentface expressionaamsvmcovid-19telehealth.TechnologyTScienceQENZanco Journal of Pure and Applied Sciences, Vol 33, Iss 5, Pp 31-42 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pain assessment face expression aam svm covid-19 telehealth. Technology T Science Q
spellingShingle	pain assessment face expression aam svm covid-19 telehealth. Technology T Science Q dalya abdullah anwar Real Time Pain Detection Using Facial Action Units in Telehealth System
description	During the Covid-19 pandemic, to reduce staff exposure to ill people, minimize the impact of patient surges on facilities, and preserve personal protective equipment, the recommendations are made by the World Health Organization to change the way that health care is delivered. Several telehealth systems are utilized including live audio-video interaction or real-time telephone typically with a patient using a computer, smartphone, or tablet. During these appointments, the doctors need to know the pain levels of the patient to be able to prescribe the correct medicine and diagnose the disease proficiently. In this paper, a real-time 4- pain levels recognition based on facial expression during telehealth is proposed. Generally, the pain is measured via verbal communication, normally the patient’s self-report. However, if the patient has a disability and unable to communicate with others due to being impaired mentally or having breathing problems or the child self-reporting may not be a perfect way to measure the pain. The proposed system consists of two methods to detect pain from a patient’s facial expressions. The AAM_Based method detects the face and facial landmarks from each video frame using Active Appearance Model AAM, these landmarks are used to compute the facial features. The AU_Based method uses Facial Action Units AU which objectively describes facial muscle activations that are considered as Region of Interest. Support Vector Machine classifier is utilized to detect the levels of pain. A labeled dataset such as Biovid is used to train test, and the AAM_based method, while and UNBC is used for the second method. The findings show that it is possible to depend on facial expression to detect pain level 1 and level 4 very accurately, while it is very tricky to detect pain level 2, and 3 because the AUs for them are similar for most of the patients.
format	article
author	dalya abdullah anwar
author_facet	dalya abdullah anwar
author_sort	dalya abdullah anwar
title	Real Time Pain Detection Using Facial Action Units in Telehealth System
title_short	Real Time Pain Detection Using Facial Action Units in Telehealth System
title_full	Real Time Pain Detection Using Facial Action Units in Telehealth System
title_fullStr	Real Time Pain Detection Using Facial Action Units in Telehealth System
title_full_unstemmed	Real Time Pain Detection Using Facial Action Units in Telehealth System
title_sort	real time pain detection using facial action units in telehealth system
publisher	Salahaddin University-Erbil
publishDate	2021
url	https://doaj.org/article/04b5ddb983a943209f8abf8c2a835226
work_keys_str_mv	AT dalyaabdullahanwar realtimepaindetectionusingfacialactionunitsintelehealthsystem
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Real Time Pain Detection Using Facial Action Units in Telehealth System

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