Enhanced Pain Detection and Movement of Motion with Data Augmentation based on Deep Learning

Pikulkaew, Kornprom; Chouvatut, Varin

doi:10.1109/kst51265.2021.9415827

Cited by 5 publications

(1 citation statement)

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“…The authors explore several ways of using FACS Action Units AUs to combine them with their proposed extended multitask learning model (Xu & de Sa, 2020). Deep learning is utilized to train dataset and activity method to guide patient orientation, the method separated pain thresholds into 3 stages: no pain, start having pain, having pain (Pikulkaew, 2021). Deep convolution neural network DCNN is employed to detect pain, the proposed method is evaluated and tested using UNBC-McMaster shoulder pain dataset (Semwal et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Real Time Pain Detection Using Facial Action Units in Telehealth System

Anwar

2021

ZJPAS

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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 4pain 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.

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Section: Introductionmentioning

confidence: 99%