Accepted for/Published in: JMIR AI
Date Submitted: Sep 22, 2023
Open Peer Review Period: Sep 22, 2023 - Nov 17, 2023
Date Accepted: Jul 23, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Survey on Pain Detection using Machine Learning Models
ABSTRACT
Pain, as a highly individualized experience, stands as a primary reason driving individuals towards seeking medical attention. The assessment of pain traditionally relies upon self-reported or input from caregivers. Yet, the former proves inadequate when dealing with non-communicative patients, while the latter may suffer from subjective caregiver biases, introducing errors. Furthermore, human-resource and time-resource make periodic reporting impractical. Consequently, the emergence of automated tools for pain assessment holds substantial potential. Multiple studies have been conducted to assess the feasibility of automated pain evaluation. In this comprehensive survey, we commence by offering an overview of pain and its underlying mechanisms. Subsequently, we examine existing literature encompassing various modalities proposed for automated pain recognition. These modalities encompass facial expressions, physiological signals, audio, and pupil dilation. Concluding our survey, we delve into the prevalent challenges and propose directions for the progressive advancement of this field.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.