Pain detection from facial expressions using domain adaptation technique

Rathee, Neeru; Pahal, Sudesh; Sheoran, Poonam

doi:10.1007/s10044-021-01025-4

Cited by 5 publications

(7 citation statements)

References 30 publications

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“…However, despite the promising pain assessment results gained by using the machine learning-based methods [1][2][3][4], most of them still need more improvement to reach satisfactory results. A very encouraging alternative is the use of deep learning models which have outperformed the machine learning models in pain assessment tasks [5][6][7][8][9][10][11][12][13].…”

Section: Machine-learning-based Methodsmentioning

confidence: 99%

“…This is principally due to the success of deep learning models for data classification and the availability of large pain datasets. Most deep learning-based pain assessment studies [7][8][9][10]12] have utilized a variation of the successful Convolutional Neural Network (CNN) [49] (see Table 3). Many of them have exploited more sophisticated recent deep learning models (e.g., ResNet, DenseNet and InceptionV3) [5,6,11,13].…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…Additionally, evaluating the UNBC-McMaster dataset, Rathee et al [9] have suggested a CNN-based pain assessment approach with an estimation accuracy of 90.3%. This technique uses an improved version of the Model Agnostic Meta-Learning (MAML++) module, which aids in efficiently initializing the CNN model parameter so that it can rapidly converge to the optimal performance with the fewest number of images and the least amount of time.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…The availability of many face images and/or video datasets for pain assessment has driven recent advances in the field of automatic pain assessment. The UNBC-McMaster Shoulder Pain Expression Archive Database (UNBC-McMaster) [44] is the one of the most widely utilized of these datasets [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][17][18][19][20][21]. This dataset was gathered from 25 adult participants suffering from shoulder pain, which form 48,398 RGB frames issued from 200 variable-length videos (see details in Table 1).…”

Section: Publicly Accessible Pain Assessment Datasetsmentioning

confidence: 99%

“…The conventional machine learning-based methods [1][2][3][4] manually extract handcrafted facial features, then classify them using conventional machine learning algorithms such as Support Vector Machines (SVMs) [47] and K-Nearest Neighbors (KNNs) [48]. However, deep learning-based methods [5][6][7][8][9][10][11][12][13] use deep learning models (e.g., Convolutional Neural Networks (CNNs) [49], Residual neural network (ResNet) [50], VGG [51] and Inception Network [52]) for both feature extraction and model-learning tasks. Recently, hybrid modelbased methods [14][15][16][17][18][19][20][21], which combine machine learning and/or deep learning have been proposed for the pain assessment task.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Automatic Pain Assessment from Facial Information Using Machine Learning

Ben Aoun

2024

Technologies

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Pain assessment has become an important component in modern healthcare systems. It aids medical professionals in patient diagnosis and providing the appropriate care and therapy. Conventionally, patients are asked to provide their pain level verbally. However, this subjective method is generally inaccurate, not possible for non-communicative people, can be affected by physiological and environmental factors and is time-consuming, which renders it inefficient in healthcare settings. So, there has been a growing need to build objective, reliable and automatic pain assessment alternatives. In fact, due to the efficiency of facial expressions as pain biomarkers that accurately expand the pain intensity and the power of machine learning methods to effectively learn the subtle nuances of pain expressions and accurately predict pain intensity, automatic pain assessment methods have evolved rapidly. This paper reviews recent spatial facial expressions and machine learning-based pain assessment methods. Moreover, we highlight the pain intensity scales, datasets and method performance evaluation criteria. In addition, these methods’ contributions, strengths and limitations will be reported and discussed. Additionally, the review lays the groundwork for further study and improvement for more accurate automatic pain assessment.

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