Recognition of Neonatal Facial Expressions of Acute Pain Using Boosted Gabor Features

Yuan, Linwang; Bao, Forrest Sheng; Lu, Guanming

doi:10.1109/ictai.2008.122

Cited by 5 publications

(8 citation statements)

References 16 publications

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“…In view of the fact that, feature extractions for facial recognitions through Gabor filters [20,21] provides good results, Gabor waveform oriented feature extraction method is executed here. The relation of two dimensional Gabor kernels is mentioned below.…”

Section: The Gabor Filter Methodsmentioning

confidence: 99%

A Hybrid Pain Detection Technique using SVM Parameter Optimization and Contourlet-Gabor Transform Feature Fusion

Saini¹,

Mandal²,

Sharma³

2019

IJITEE

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This paper highlights automatic pain expression recognition from facial images of pain. This technique is frequently used in healthcare applications. The research detects the pain expression by an effective recognition method. It is based on the concepts of fusion of contourlet transform with Gabor transform for feature extraction, and classification of expressions by using SVM. The ABC algorithm and GA is implemented for optimizing SVM parameters. In order to authenticate the precision and stoutness of the system, a number of experiments have been done on two databases namely UNBC shoulder pain database and IFD-CEERI database. It's provided throughout the simulation techniques. The suggested strategy is actually effective in recognition of pain expression in terms of accuracy

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Section: The Gabor Filter Methodsmentioning

confidence: 99%

A Hybrid Pain Detection Technique using SVM Parameter Optimization and Contourlet-Gabor Transform Feature Fusion

Saini¹,

Mandal²,

Sharma³

2019

IJITEE

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“…Various pain assessment measures (tools, instruments, etc.) based on facial expressions of pain have been developed (Yuan, Bao, & Guanming, 2008). This has proved to be a popular area of research as an automated recognition of facial expressions of pain does not require reliance on health professionals that has great medical significance.…”

Section: Facial Recognitionmentioning

confidence: 99%

“…This has proved to be a popular area of research as an automated recognition of facial expressions of pain does not require reliance on health professionals that has great medical significance. The objective of the facial recognition study done by Yuan et al is to bypass the observational problems by developing a machine classification system to diagnose neonatal facial expressions of pain (Yuan et al, 2008). In this particular study, they employ an expressive feature, the Gabor feature, to the representation of neonatal faces.…”

Section: Facial Recognitionmentioning

confidence: 99%

“…In this particular study, they employ an expressive feature, the Gabor feature, to the representation of neonatal faces. Each neonatal facial image is convoluted with the 2D Gabor Filters to extract 412,160 Gabor features (Yuan et al, 2008). The Gabor features are obtained by convoluting the image with a series of Gabor filers.…”

Section: Facial Recognitionmentioning

confidence: 99%

“…The Gabor features are obtained by convoluting the image with a series of Gabor filers. Gabor filters extract features from different orientation and different scales (Yuan et al, 2008). Image analysis with Gabour filters is thought to be similar to perception in the human visual system, which is why it is used in this case.…”

Section: Facial Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

Automated partial premature infant pain profile scoring using big data analytics

Naik

McGregor

James

2017

2017 IEEE Life Sciences Conference (LSC)

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Lack of valid and reliable pain assessment in the neonatal population has become a significant challenge in the Neonatal Intensive Care Unit (NICU). Current practice in the NICU involves the meticulous, time-consuming and potentially bias process of manual interpretation of pain scores. In an attempt to forego the manual scoring system, this thesis presents an initial framework to automate a partial pain score for newborn infants using big data analytics that automates the analysis of high speed physiological data. The design of the novel Artemis Premature Infant Pain Profile (APIPP) is proposed in this thesis. An ethically approved retrospective clinical research study was performed to calculate APIPP scores from premature infant data collected from the Artemis platform. Using the Premature Infant Pain Profile (PIPP) as the base gold standard scale, scoring techniques were automated to create data abstractions from the physiological streams of Heart Rate (HR) and Oxygen Saturation (SpO 2). These were then brought together to compute an automated partial pain score (APIPP) that was based on gestational age, HR and SpO 2. Through the retrospective clinical research study, and to evaluate the effectiveness and feasibility of automating the scale in the future, APIPP was retrospectively compared with the PIPP which was manually scored by nursing staff at The Hospital for Sick Children, Toronto. Furthermore, the characteristics in HR were also assessed in a thorough manner by preforming statistical tests to assess the resourcefulness of HR as a measure to identify a pain response. Future research will focus on the clinical validation of this work by carrying out prospective research to implement an algorithm based on the design proposed in this thesis that can be integrated into a clinical decision support system named Artemis.

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Scope of physiological and behavioural pain assessment techniques in children – a review

Subramaniam

Doss

Chanderasekar

et al. 2018

Healthcare Technology Letters

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Pain is an unpleasant subjective experience. At present, clinicians are using self-report or pain scales to recognise and monitor pain in children. However, these techniques are not efficient to observe the pain in children having cognitive disorder and also require highly skilled observers to measure pain. Using these techniques it is also difficult to choose the analgesic drug dosages to the patients after surgery. Thus, this conceptual work explains the demand for automatic coding techniques to evaluate pain and also it documents some evidence of techniques that act as an alternative approach for objectively determining pain in children. In this review, some good indicators of pain in children are explained in detail; they are facial expressions from an RGB image, thermal image and also feature from well proven physiological signals such as electrocardiogram, skin conductance, body temperature, surgical pleth index, pupillary reflex dilation, analgesia nociception index, photoplethysmography, perfusion index etc.

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Recognition of Neonatal Facial Expressions of Acute Pain Using Boosted Gabor Features

Cited by 5 publications

References 16 publications

A Hybrid Pain Detection Technique using SVM Parameter Optimization and Contourlet-Gabor Transform Feature Fusion

A Hybrid Pain Detection Technique using SVM Parameter Optimization and Contourlet-Gabor Transform Feature Fusion

Automated partial premature infant pain profile scoring using big data analytics

Scope of physiological and behavioural pain assessment techniques in children – a review

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