Assessment of Pain Using Facial Pictures Taken with a Smartphone

Adibuzzaman, Mohammad; Ostberg, Colin; Ahamed, Sheikh Iqbal; Povinelli, Richard J.; Sindhu, Bhagwant S.; Love, Richard R.; Kawsar, Ferdaus; Ahsan, Golam Mushih Tanimul

doi:10.1109/compsac.2015.150

Cited by 12 publications

(12 citation statements)

References 23 publications

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“…Irani et al [82] and Haque et al [80] not only use RGB images, but also thermal and depth images for analyzing facial expression. Adibuzzaman et al [73] use photographs taken with smartphone cameras, which are heterogeneous regarding quality, resolution, sharpness, lighting etc. Generally, cameras suffer from a limited field of view (which also may be occluded or inadequately lighted) and interpreting images is more complex than other sensor signals.…”

Section: Camera-based Approachesmentioning

confidence: 99%

Automatic Recognition Methods Supporting Pain Assessment: A Survey

Werner

Lopez-Martinez

Walter³

et al. 2022

IEEE Trans. Affective Comput.

167

119

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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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Section: Camera-based Approachesmentioning

confidence: 99%

Automatic Recognition Methods Supporting Pain Assessment: A Survey

Werner

Lopez-Martinez

Walter³

et al. 2022

IEEE Trans. Affective Comput.

167

119

View full text Add to dashboard Cite

show abstract

“…Pain Care [310] is a mobile app that assists patients with chronic pain in the management of their symptoms, drugs and communication with medics. Another example is the app described in [311], which performs an evaluation of pain using facial images acquired with a smartphone. Images are decomposed in vector subspaces with sub-images as characteristic vectors defining the image.…”

Section: Digital Resources For the Evaluation Of 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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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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“…Then the range of pain level (0-10) was transformed into three (3) categorical levels: low (0-3), mid (4-6) and high (7)(8)(9)(10). This classification into categories is similar to the Brief Pain Inventory which has been proposed and validated across different cultures [29]. Residuals of each level were observed for four (4) subjects.…”

Section: Cross-sectional Evaluationmentioning

confidence: 99%

“…Nepal and South Dakota in United States[10] [29]. The data collection protocol wasapproved at Marquette University and by the responsible ethical review boards in Bangladesh, Nepal and Rapid City, South Dakota in the United States[10][29].…”

mentioning

confidence: 99%