EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions

Egede, Joy; Song, Siyang; Olugbade, Temitayo; Wang, Chongyang; Williams, Amanda C de C; Meng, Hongying; Aung, Min S. H.; Lane, Nicholas D.; Valstar, Michel; Bianchi-Berthouze, Nadia

doi:10.1109/fg47880.2020.00078

Cited by 26 publications

(9 citation statements)

References 56 publications

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“…Nevertheless, the C2 and C3 configurations surpass the baseline in terms of PCC. Our results are not directly comparable to those reported by the Challenge organisers [8], as we have evaluated different features. Nevertheless, our results do not beat the best baseline system of the Challenge organisers, which achieved a CCC of 0.180 using the validation partition.…”

Section: Resultscontrasting

confidence: 93%

“…For the Challenge [8], only the features extracted from the facial videos have been made available to participants. The available facial features include facial landmarks, head pose, Histogram of Oriented Gradient (HOG) features, action unit intensity values and occurrence extracted with OpenFace [3], and deep-learnt feature representations extracted using VGG-16 [23] and ResNet-50 [14] pre-trained models.…”

Section: Emopain Datasetmentioning

confidence: 99%

“…Herein, we present our contribution to the Pain Intensity Estimation from Facial Expressions task of the EMOPAIN 2020 Challenge 1 [8]. In this work, we investigate the use of specific Action Units (AUs) of the Facial Action Coding System (FACS) [10] to train a RNN able to predict the pain intensity on individuals reported on a continuous 11-point scale.…”

Section: Introductionmentioning

confidence: 99%

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A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions

Mallol-Ragolta

Liu

Cummins

et al. 2020

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)

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The high prevalence of chronic pain in society raises the need to develop new digital tools that can automatically and objectively assess pain intensity in individuals. These tools can contribute to an optimisation of clinical resources, as they offer cost-effective solutions for early detection, continuous monitoring, and treatment personalisation by utilising Artificial Intelligence techniques. In this work, we present our contribution to the Pain Intensity Estimation from Facial Expressions task of the EMOPAIN 2020 Challenge. Specifically, we compare the performance of Recurrent Neural Networks trained with standard or Curriculum Learning (CL) approaches to predict the pain intensity level of individuals reported in an 11-point scale from facial expressions. The results obtained using the test partition support the use of CL-based approaches in the automatic prediction of pain from facial features. The best model trained using a CL approach achieved a Concordance Correlation Coefficient (CCC) of 0.196 in the test partition, while the model trained using a standard approach, without CL, achieved a CCC of 0.174. In terms of CCC, these results respectively represent an improvement of 0.136 and 0.114 on the best results of the baseline system reported by the Challenge organisers using the test partition.

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Section: Resultscontrasting

confidence: 93%

Section: Emopain Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions

Mallol-Ragolta

Liu

Cummins

et al. 2020

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)

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“…Although in everyday pain experience we encounter associations between body movement and pain, the communicative functions of body movements in relation to pain have been fairly unexplored in automatic pain assessment. Notable exceptions are to be found in the work of Aung et al [(4), see also Egede et al (5)] who found association between pain and certain bodily protective behaviors, such as guarding/stiffness and bracing/support.…”

Section: Introductionmentioning

confidence: 99%

Computer Mediated Automatic Detection of Pain-Related Behavior: Prospect, Progress, Perils

Hammal

2021

Front. Pain Res.

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Pain is often characterized as a fundamentally subjective phenomenon; however, all pain assessment reduces the experience to observables, with strengths and limitations. Most evidence about pain derives from observations of pain-related behavior. There has been considerable progress in articulating the properties of behavioral indices of pain; especially, but not exclusively those based on facial expression. An abundant literature shows that a limited subset of facial actions, with homologs in several non-human species, encode pain intensity across the lifespan. Unfortunately, acquiring such measures remains prohibitively impractical in many settings because it requires trained human observers and is laborious. The advent of the field of affective computing, which applies computer vision and machine learning (CVML) techniques to the recognition of behavior, raised the prospect that advanced technology might overcome some of the constraints limiting behavioral pain assessment in clinical and research settings. Studies have shown that it is indeed possible, through CVML, to develop systems that track facial expressions of pain. There has since been an explosion of research testing models for automated pain assessment. More recently, researchers have explored the feasibility of multimodal measurement of pain-related behaviors. Commercial products that purport to enable automatic, real-time measurement of pain expression have also appeared. Though progress has been made, this field remains in its infancy and there is risk of overpromising on what can be delivered. Insufficient adherence to conventional principles for developing valid measures and drawing appropriate generalizations to identifiable populations could lead to scientifically dubious and clinically risky claims. There is a particular need for the development of databases containing samples from various settings in which pain may or may not occur, meticulously annotated according to standards that would permit sharing, subject to international privacy standards. Researchers and users need to be sensitive to the limitations of the technology (for e.g., the potential reification of biases that are irrelevant to the assessment of pain) and its potentially problematic social implications.

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“…The main advantage of using facial expression to detect the pain levels is to reduce the distress caused by recording the brain activities or other physiological signals such as electrocardiogram ECG and electromyogram EMG that required sensors to be contacted directly to patient"s body/skin (Kunz and Lautenbacher, 2019). An international competition aimed to create a platform for the comparison of multimedia processing methods of chronic pain assessment form human behavior and multi-model machine learning is conducted by the EmoPain 2020 challenge team (Egede et al, 2020).…”

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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EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions

Cited by 26 publications

References 56 publications

A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions

A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions

Computer Mediated Automatic Detection of Pain-Related Behavior: Prospect, Progress, Perils

Real Time Pain Detection Using Facial Action Units in Telehealth System

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