Pain Detection Using Deep Learning with Evaluation System

Pikulkaew, Kornprom; Boonchieng, Ekkarat; Boonchieng, Waraporn; Chouvatut, Varin

doi:10.1007/978-981-15-5859-7_42

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…For most of the shoulder pain research, the labeled faces were taken for assessing the level of the pain as one of three classes, namely no pain, medium pain and high pain, or even more. Patients with shoulder pain underwent some physical tests on the abnormal shoulder, and appropriate levels of pain were considered from relative expressions of the face [53]. Different motion tests were conducted on patients to know the level of pain by the physiotherapist.…”

Section: Shoulder Painmentioning

confidence: 99%

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Madanu

Abbod

Hsiao³

et al. 2022

Technologies

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Pain is a complex term that describes various sensations that create discomfort in various ways or types inside the human body. Generally, pain has consequences that range from mild to severe in different organs of the body and will depend on the way it is caused, which could be an injury, illness or medical procedures including testing, surgeries or therapies, etc. With recent advances in artificial-intelligence (AI) systems associated in biomedical and healthcare settings, the contiguity of physician, clinician and patient has shortened. AI, however, has more scope to interpret the pain associated in patients with various conditions by using any physiological or behavioral changes. Facial expressions are considered to give much information that relates with emotions and pain, so clinicians consider these changes with high importance for assessing pain. This has been achieved in recent times with different machine-learning and deep-learning models. To accentuate the future scope and importance of AI in medical field, this study reviews the explainable AI (XAI) as increased attention is given to an automatic assessment of pain. This review discusses how these approaches are applied for different pain types.

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Section: Shoulder Painmentioning

confidence: 99%

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Madanu

Abbod

Hsiao³

et al. 2022

Technologies

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“…Pikulkaew et al [12] applied the Wasserstein generative adversarial network (WGAN) to human faces and discovered that it might improve the pain discovery technique's high efficiency. They also used the WGAN to improve pain detection techniques, which could increase efficiency due to the limited data of data sets [13].…”

Section: Introductionmentioning

confidence: 99%

2D Facial Expression and Movement of Motion for Pain Identification With Deep Learning Methods

Pikulkaew

Boonchieng

et al. 2021

IEEE Access

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Pain is an inevitable part of life, no matter one's age or gender. This study lends support for the importance of facial expression technology in assisting individuals with pain. The self-report system commonly used to detect discomfort is ineffective and cannot be used by all ages; thus, using a standardized formula for measuring pain would resolve this problem. Because it is easy to use and has a high degree of precision, facial monitoring technology is an important tool for measuring pain. Using deep learning techniques, this article suggests using 2D facial expressions and motion to sense pain. In this study, we made use of sequential pictures from the University of Northern British Columbia (UNBC) dataset. Deep learning has been used to train data and detect motion to aid in patient orientation. Our mechanism is capable of classifying pain into three categories: not painful, becoming be painful, and painful. Additionally, the system's performance is evaluated by comparing the findings to those received from a specialist physician. The precision of the no-pain classification is 99.75 percent, the initial pain classification is 92.93 percent, and the pain classification is 95.15 percent. Our study has developed a gold standard for pain monitoring to test for pain before hospitalization. It is straightforward, cost effective, and readily comprehended by both the general population and health care professionals. Additionally, this analysis may be applied to other screening methods, such as pain detection for infectious diseases.

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Real-Time Pain Detection Using Deep Convolutional Neural Network for Facial Expression and Motion

Pikulkaew

Boonchieng

2022

Proceedings of Seventh International Congress on Information and Communication Technology

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Pain Detection Using Deep Learning with Evaluation System

Cited by 8 publications

References 12 publications

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

2D Facial Expression and Movement of Motion for Pain Identification With Deep Learning Methods

Real-Time Pain Detection Using Deep Convolutional Neural Network for Facial Expression and Motion

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