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Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework
A sentiment analysis system has been proposed in this paper for pain detection using cutting edge techniques in a smart healthcare framework. This proposed system may be eligible for detecting pain sentiments by analyzing facial expressions on the human face. The implementation of the proposed syste...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799976/ https://www.ncbi.nlm.nih.gov/pubmed/35125934 http://dx.doi.org/10.1007/s10586-022-03552-z |
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author | Ghosh, Anay Umer, Saiyed Khan, Muhammad Khurram Rout, Ranjeet Kumar Dhara, Bibhas Chandra |
author_facet | Ghosh, Anay Umer, Saiyed Khan, Muhammad Khurram Rout, Ranjeet Kumar Dhara, Bibhas Chandra |
author_sort | Ghosh, Anay |
collection | PubMed |
description | A sentiment analysis system has been proposed in this paper for pain detection using cutting edge techniques in a smart healthcare framework. This proposed system may be eligible for detecting pain sentiments by analyzing facial expressions on the human face. The implementation of the proposed system has been divided into four components. The first component is about detecting the face region from the input image using a tree-structured part model. Statistical and deep learning-based feature analysis has been performed in the second component to extract more valuable and distinctive patterns from the extracted facial region. In the third component, the prediction models based on statistical and deep feature analysis derive scores for the pain intensities (no-pain, low-pain, and high-pain) on the facial region. The scores due to the statistical and deep feature analysis are fused to enhance the performance of the proposed method in the fourth component. We have employed two benchmark facial pain expression databases during experimentation, such as UNBC-McMaster shoulder pain and 2D Face-set database with Pain-expression. The performance concerning these databases has been compared with some existing state-of-the-art methods. These comparisons show the superiority of the proposed system. |
format | Online Article Text |
id | pubmed-8799976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-87999762022-01-31 Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework Ghosh, Anay Umer, Saiyed Khan, Muhammad Khurram Rout, Ranjeet Kumar Dhara, Bibhas Chandra Cluster Comput Article A sentiment analysis system has been proposed in this paper for pain detection using cutting edge techniques in a smart healthcare framework. This proposed system may be eligible for detecting pain sentiments by analyzing facial expressions on the human face. The implementation of the proposed system has been divided into four components. The first component is about detecting the face region from the input image using a tree-structured part model. Statistical and deep learning-based feature analysis has been performed in the second component to extract more valuable and distinctive patterns from the extracted facial region. In the third component, the prediction models based on statistical and deep feature analysis derive scores for the pain intensities (no-pain, low-pain, and high-pain) on the facial region. The scores due to the statistical and deep feature analysis are fused to enhance the performance of the proposed method in the fourth component. We have employed two benchmark facial pain expression databases during experimentation, such as UNBC-McMaster shoulder pain and 2D Face-set database with Pain-expression. The performance concerning these databases has been compared with some existing state-of-the-art methods. These comparisons show the superiority of the proposed system. Springer US 2022-01-29 2023 /pmc/articles/PMC8799976/ /pubmed/35125934 http://dx.doi.org/10.1007/s10586-022-03552-z Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Ghosh, Anay Umer, Saiyed Khan, Muhammad Khurram Rout, Ranjeet Kumar Dhara, Bibhas Chandra Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework |
title | Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework |
title_full | Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework |
title_fullStr | Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework |
title_full_unstemmed | Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework |
title_short | Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework |
title_sort | smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799976/ https://www.ncbi.nlm.nih.gov/pubmed/35125934 http://dx.doi.org/10.1007/s10586-022-03552-z |
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