An Approach for Automatic Pain Detection through Facial Expression

Roy, Saptarshi; Bhowmik, Mrinal Kanti; Saha, Priya; Ghosh, Anjan

doi:10.1016/j.procs.2016.04.072

Cited by 39 publications

(21 citation statements)

References 10 publications

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“…These include face shape localisation and landmark detection ( 73,74 ), as well as image classification (e.g. 75 ). Whilst these techniques have been developed, and subsequently refined, for use with human faces, associated algorithms are potentially easily adapted and applied to non-human animal faces (for examples see 5,6,76 ).…”

Section: Discussionmentioning

confidence: 99%

Geometric morphometrics for the study of facial expressions in non-human animals, using the domestic cat as an exemplar

Finka

Luna

Brondani

et al. 2019

Sci Rep

102

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Facial expression is a common channel for the communication of emotion. However, in the case of non-human animals, the analytical methods used to quantify facial expressions can be subjective, relying heavily on extrapolation from human-based systems. Here, we demonstrate how geometric morphometrics can be applied in order to overcome these problems. We used this approach to identify and quantify changes in facial shape associated with pain in a non-human animal species. Our method accommodates individual variability, species-specific facial anatomy, and postural effects. Facial images were captured at four different time points during ovariohysterectomy of domestic short haired cats (n = 29), with time points corresponding to varying intensities of pain. Images were annotated using landmarks specifically chosen for their relationship with underlying musculature, and relevance to cat-specific facial action units. Landmark data were subjected to normalisation before Principal Components (PCs) were extracted to identify key sources of facial shape variation, relative to pain intensity. A significant relationship between PC scores and a well-validated composite measure of post-operative pain in cats (UNESP-Botucatu MCPS tool) was evident, demonstrating good convergent validity between our geometric face model, and other metrics of pain detection. This study lays the foundation for the automatic, objective detection of emotional expressions in a range of non-human animal species.

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Section: Discussionmentioning

confidence: 99%

Geometric morphometrics for the study of facial expressions in non-human animals, using the domestic cat as an exemplar

Finka

Luna

Brondani

et al. 2019

Sci Rep

102

View full text Add to dashboard Cite

show abstract

“…The researchers' method to create a simple, cost-effective method to detect pain was successful. Principal Component Analysis (PCA), which uses eigenimage pain detection, was used with Gabor filtering by Roy et al to automatically detect pain [ 34 ]. In this approach, the entire face was first considered as an eigenimage, and then specific features were extracted from different locations in the face.…”

Section: Review Of the Literaturementioning

confidence: 99%

Facial Pain Expression Recognition in Real-Time Videos

Dutta

Nachamai²

2018

Journal of Healthcare Engineering

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Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%–55% and an improved processing time of 20%–25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%–100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC‐McMaster Shoulder Pain Database as well.

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“…Recently, pain detection and its connection with the brain, and pain classification by features have received attention worldwide. Accordingly, the obtained results based on different methods demonstrated that the brain could sense the pain [8][9][10][11][12][13][14][15][16][17][18][19][20]. It should be noted that the data in some studies were gathered via EEG.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis of Human Brain in the Pain State by Electroencephalography

Tavasoli

Einalou

Akhondzadeh

2021

Preprint

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Objective Pain is an unpleasant sensation that is important in all therapeutic conditions. So far, some studies have focused on pain assessment and cognition through different tests and methods. Considering the occurrence of pain causes, along with the activation of a long network in brain regions, recognizing the dynamical changes of the brain in pain states is helpful for pain detection using the electroencephalogram (EEG) signal. Therefore, the present study addressed the above-mentioned issue by applying EEG at the time of inducing phasic pain. Results Phasic pain was produced using coldness and then dynamical features via EEG were analyzed by the Recurrence Quantification Analysis (RQA) method, and finally, the Rough neural network classifier was utilized for achieving accuracy regarding detecting and categorizing pain and non-pain states, which was 95.25\(\pm\)4%. The simulation results confirmed that cerebral behaviors are detectable during pain. In addition, the high accuracy of the classifier for evaluating the dynamical features of the brain during pain occurrence is one of the most merits of the proposed method. Eventually, pain detection can improve medical methods.

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An Approach for Automatic Pain Detection through Facial Expression

Cited by 39 publications

References 10 publications

Geometric morphometrics for the study of facial expressions in non-human animals, using the domestic cat as an exemplar

Geometric morphometrics for the study of facial expressions in non-human animals, using the domestic cat as an exemplar

Facial Pain Expression Recognition in Real-Time Videos

Dynamic Analysis of Human Brain in the Pain State by Electroencephalography

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