Automatic spontaneous pain recognition using supervised classification learning algorithms

Rupenga, Moses; Vadapalli, Hima

doi:10.1109/robomech.2016.7813150

Cited by 6 publications

(8 citation statements)

References 17 publications

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“…Based on these findings, it was observed that using suitable conditions, automated pain recognition systems to detect and identify pain had the potential to be extremely efficient. This highlighted their usefulness and corresponded to earlier studies [ 38 – 40 ].…”

Section: Discussionsupporting

confidence: 89%

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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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Section: Discussionsupporting

confidence: 89%

“…The extraction that occurred was faulty, and the HR was reduced. Lower accuracy was noted for faces that were not looking directly at the recording device, which matched the findings stated by Rupenga and Vadapalli [ 40 ]. Sikka et al reported that motion in front of the camera while recording reduced the accuracy of the system [ 43 ].…”

Section: Discussionsupporting

confidence: 87%

Facial Pain Expression Recognition in Real-Time Videos

Dutta

Nachamai²

2018

Journal of Healthcare Engineering

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“…In this domain features can further be distinguished as (1) frame-level features vs. features that integrate information over time (time-window or video level), (2) geometric vs. appearance features, and (3) local vs. global features. A variety of frame-level features have been used for recognizing facial pain expression: (1) generic shape features (most often plain landmark coordinates) [1], [2], [5], [8], [12], [16], [17], [20]- [22], [25], [31], [33], [34], [38], [40], [42], [63], [89]; (2) generic appearance features, which include plain pixel representations ("SAPP", "CAPP", and similar) [1], [2], [8], [20], [21], [38], [43], [66], [73], Local Binary Pattern (LBP) [3], [12]- [14], [26], [30], [35], [39], [41], [42], [63], Histogram of Oriented Gradients (HOG) [4], [5], [14], [26], [60], [62], Gabor [18],…”

Section: Frame-level Facial Expression Featuresmentioning

confidence: 99%

Automatic Recognition Methods Supporting Pain Assessment: A Survey

Werner

Lopez-Martinez

Walter³

et al. 2022

IEEE Trans. Affective Comput.

168

135

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Pain is a complex phenomenon, involving sensory and emotional experience, that is often poorly understood, especially in infants, anesthetized patients, and others who cannot speak. Technology supporting pain assessment has the potential to help reduce suffering; however, advances are needed before it can be adopted clinically. This survey paper assesses the state of the art and provides guidance for researchers to help make such advances. First, we overview pain's biological mechanisms, physiological and behavioral responses, emotional components, as well as assessment methods commonly used in the clinic. Next, we discuss the challenges hampering the development and validation of pain recognition technology, and we survey existing datasets together with evaluation methods. We then present an overview of all automated pain recognition publications indexed in the Web of Science as well as from the proceedings of the major conferences on biomedical informatics and artificial intelligence, to provide understanding of the current advances that have been made. We highlight progress in both non-contact and contact-based approaches, tools using face, voice, physiology, and multi-modal information, the importance of context, and discuss challenges that exist, including identification of ground truth. Finally, we identify underexplored areas such as chronic pain and connections to treatments, and describe promising opportunities for continued advances.

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“…Summary of the learning approaches that have been developed and tested for automatic pain detection from facial expressions.Brahnam et al[79], Monwar and Rezaei[80], Brahnam et al[81], Lu et al[51], Ashraf et al[82], Lucey et al[83], Siebers et al[84], Nanni et al[85], Gholami et al[86], Monwar and Rezaei[87], Wei and Li-min[88], Lucey et al[18], Lucey et al[89], Werner et al[90], Chen et al[91], Khan et al[92], Pedersen[93], Neshov and Manolova[94], Rathee and Ganotra[95], Aung et al[27], Kharghanian et al[96], Roy et al[97], Rupenga and Vadapalli[98], Meawad et al[99], Alphonse and Dharma[100] …”

mentioning

confidence: 99%

“…Summary of spatial representations extracted directly from facial images for automatic pain detection. Berthouze[102], Ghasemi et al[74], Aung et al[27], Rupenga and Vadapalli[98], Liu et al[110], Lopez-Martinez et al[118] facial landmark distances Romera-Paredes et al[108], Meawad et al[99] facial landmark distances and angles Niese et al[54], Siebers et al[84] …”

mentioning

confidence: 99%

Automatic Detection of Pain from Facial Expressions: A Survey

Hassan

Seus

Wollenberg

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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Pain sensation is essential for survival, since it draws attention to physical threat to the body. Pain assessment is usually done through self-reports. However, self-assessment of pain is not available in the case of noncommunicative patients, and therefore, observer reports should be relied upon. Observer reports of pain could be prone to errors due to subjective biases of observers. Moreover, continuous monitoring by humans is impractical. Therefore, automatic pain detection technology could be deployed to assist human caregivers and complement their service, thereby improving the quality of pain management, especially for noncommunicative patients. Facial expressions are a reliable indicator of pain, and are used in all observer-based pain assessment tools. Following the advancements in automatic facial expression analysis, computer vision researchers have tried to use this technology for developing approaches for automatically detecting pain from facial expressions. This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions. The survey covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and finally, the machine learning methods used.

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Automatic spontaneous pain recognition using supervised classification learning algorithms

Cited by 6 publications

References 17 publications

Facial Pain Expression Recognition in Real-Time Videos

Facial Pain Expression Recognition in Real-Time Videos

Automatic Recognition Methods Supporting Pain Assessment: A Survey

Automatic Detection of Pain from Facial Expressions: A Survey

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