Deep-Learning-Based Models for Pain Recognition: A Systematic Review

Al-Eidan, Rasha M. BinSultan; Al‐Khalifa, Hend S.; Al-Salman, AbdulMalik S.

doi:10.3390/app10175984

Cited by 23 publications

(9 citation statements)

References 31 publications

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“…The measurements were also guided by experts in a highly controlled environment. Despite the increasing interest in automatic pain recognition, a major challenge for advancing this area of research is still the lack of research in real environments [19,27,32]. In home measurements, pain provocation must be designed so that an application can guide it and it must be applicable without continuous supervision.…”

Section: Discussionmentioning

confidence: 99%

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Pain fingerprinting using multimodal sensing: pilot study

Keskinarkaus

Yang

Fylakis

et al. 2021

Multimed Tools Appl

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Pain is a complex phenomenon, the experience of which varies widely across individuals. At worst, chronic pain can lead to anxiety and depression. Cost-effective strategies are urgently needed to improve the treatment of pain, and thus we propose a novel home-based pain measurement system for the longitudinal monitoring of pain experience and variation in different patients with chronic low back pain. The autonomous nervous system and audio-visual features are analyzed from heart rate signals, voice characteristics and facial expressions using a unique measurement protocol. Self-reporting is utilized for the follow-up of changes in pain intensity, induced by well-designed physical maneuvers, and for studying the consecutive trends in pain. We describe the study protocol, including hospital measurements and questionnaires and the implementation of the home measurement devices. We also present different methods for analyzing the multimodal data: electroencephalography, audio, video and heart rate. Our intention is to provide new insights using technical methodologies that will be beneficial in the future not only for patients with low back pain but also patients suffering from any chronic pain.

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Section: Discussionmentioning

confidence: 99%

“…Recording data in real environment is challenging, as is the lack of long-term monitoring data. Several recent studies have raised this issue [19,27,32].…”

Section: Introductionmentioning

confidence: 99%

Pain fingerprinting using multimodal sensing: pilot study

Keskinarkaus

Yang

Fylakis

et al. 2021

Multimed Tools Appl

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“…Comprehensive surveys cover analysis of facial expressions [25], and body gestures [26], with the recent trend being to combine several modalities using multi-modal emotion recognition approaches [27]. In the context of pain, numerous works have addressed facial expression assessment in humans [28,29], and, notably, in infants [30].…”

Section: Introductionmentioning

confidence: 99%

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Broomé¹,

Feighelstein²,

Zamansky³

et al. 2022

Preprint

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Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go 'deeper' than tracking, and address automated recognition of animals' internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of affective states and pain in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic -classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research.

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“…defined by the CIFAR-100 [13] or Caltech Birds [29] data sets). For readers that are interested in automated pain intensity recognition, we refer to the recently published survey studies, [18] and [32], which focus on ANN-based and hand-crafted feature extraction approaches, respectively.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain Database

et al. 2022

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The work of Friedhelm Schwenker and Peter Bellmann is supported by the project Multimodal recognition of affect over the course of a tutorial learning experiment (SCHW623/7-1) funded by the German Research Foundation (DFG). Hans A. Kestler acknowledges funding from the German Science Foundation (DFG, 217328187 (SFB 1074) and 288342734 (GRK HEIST)). Hans A. Kestler also acknowledges funding from the German Federal Ministery of Education and Research (BMBF) e:MED confirm (id 01ZX1708C) and TRAN-SCAN VI -PMTR-pNET (id 01KT1901B).

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Deep-Learning-Based Models for Pain Recognition: A Systematic Review

Cited by 23 publications

References 31 publications

Pain fingerprinting using multimodal sensing: pilot study

Pain fingerprinting using multimodal sensing: pilot study

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory—An Example Based on the BioVid Heat Pain Database

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