Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 31 July 2020 / Approved: 2 August 2020 / Online: 2 August 2020 (15:28:12 CEST)

A peer-reviewed article of this Preprint also exists.

M. Al-Eidan, R.; Al-Khalifa, H.; Al-Salman, A. Deep-Learning-Based Models for Pain Recognition: A Systematic Review. Appl. Sci. 2020, 10, 5984. M. Al-Eidan, R.; Al-Khalifa, H.; Al-Salman, A. Deep-Learning-Based Models for Pain Recognition: A Systematic Review. Appl. Sci. 2020, 10, 5984.

Journal reference: Appl. Sci. 2020, 10, 5984
DOI: 10.3390/app10175984

Abstract

The traditional standards employed for pain assessment have many limitations. One such limitation is reliability because of inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges, such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years only. Furthermore, it presents the major deep-learning methods that were used in review papers. Finally, it provides a discussion of the challenges and open issues.

Subject Areas

pain assessment; pain recognition; deep learning; neural network; dataset

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