Neonatal Facial Pain Assessment Combining Hand-Crafted and Deep Features

Celona, Luigi; Manoni, Luca

doi:10.1007/978-3-319-70742-6_19

Cited by 18 publications

(19 citation statements)

References 20 publications

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“…Previously, the images must be managed by a procedure for the standardization of image characteristics [54,239]: image segmentation to recognize the face on the background, centering of facial characteristics, normalization of the arguments in the image data, image scaling to predetermined pixel dimensions, etc. If convenient, the image is cropped [241]. The color dimension is usually reduced to black and white.…”

Section: Recognition Of Facial Expressions Associated With Painmentioning

confidence: 99%

“…Local operations on image texture can also be performed with operators such as local binary patterns (LBP). Discrete cosine transform (DCT) can also be used in the image description process [241]. Other descriptors are based on Taylor series expansion of the image function, named according to the order of the derivative [240,242].…”

Section: Recognition Of Facial Expressions Associated With Painmentioning

confidence: 99%

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Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Naranjo-Hernández

Reina‐Tosina

Roa

2020

Sensors

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Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.

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Section: Recognition Of Facial Expressions Associated With Painmentioning

confidence: 99%

Section: Recognition Of Facial Expressions Associated With Painmentioning

confidence: 99%

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Naranjo-Hernández

Reina‐Tosina

Roa

2020

Sensors

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“…More recently, and introducing deep learning methods, Ref. [13] fused LBP, Histogram of Oriented Gradients (HOG), and CNNs as feature extractors, with SVM for classification, with an accuracy of 83.78% as the best result. Then, in [14], Zamzmi et al used pre-trained CNNs and a strain-based expression segmentation algorithm as a feature extractor together with a Naive Bayes (NB) classifier, obtaining a recognition accuracy of 92.71%.…”

Section: Introductionmentioning

confidence: 99%

Application of Texture Descriptors to Facial Emotion Recognition in Infants

2020

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The recognition of facial emotions is an important issue in computer vision and artificial intelligence due to its important academic and commercial potential. If we focus on the health sector, the ability to detect and control patients’ emotions, mainly pain, is a fundamental objective within any medical service. Nowadays, the evaluation of pain in patients depends mainly on the continuous monitoring of the medical staff when the patient is unable to express verbally his/her experience of pain, as is the case of patients under sedation or babies. Therefore, it is necessary to provide alternative methods for its evaluation and detection. Facial expressions can be considered as a valid indicator of a person’s degree of pain. Consequently, this paper presents a monitoring system for babies that uses an automatic pain detection system by means of image analysis. This system could be accessed through wearable or mobile devices. To do this, this paper makes use of three different texture descriptors for pain detection: Local Binary Patterns, Local Ternary Patterns, and Radon Barcodes. These descriptors are used together with Support Vector Machines (SVM) for their classification. The experimental results show that the proposed features give a very promising classification accuracy of around 95% for the Infant COPE database, which proves the validity of the proposed method.

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“…Recently, deep learning methods [15], [16] have become popular in pain classification. In case of infants, Celona and Manoni [17] proposed a framework that combines both handcrafted and deep features for classifying COPE images as pain/no-pain. Specifically, they combined LBP, HOG, pre-trained VGG-face and Mapped LBP+CNN features to represent the final feature vector.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

Salekin

Goldgof

Kasturi

et al. 2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing. The current clinical standard for assessing neonatal pain is intermittent and highly subjective. This discontinuity and subjectivity can lead to inconsistent assessment, and therefore, inadequate treatment. In this paper, we propose a multi-channel deep learning framework for assessing neonatal pain from videos. The proposed framework integrates information from two pain indicators or channels, namely facial expression and body movement, using convolutional neural network (CNN). It also integrates temporal information using a recurrent neural network (LSTM). The experimental results prove the efficiency and superiority of the proposed temporal and multi-channel framework as compared to existing similar methods.

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Neonatal Facial Pain Assessment Combining Hand-Crafted and Deep Features

Cited by 18 publications

References 20 publications

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Application of Texture Descriptors to Facial Emotion Recognition in Infants

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

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