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  1. 論文誌(ジャーナル)
  2. Vol.57
  3. No.7

Pain Level Detection From Facial Image Captured by Smartphone

https://ipsj.ixsq.nii.ac.jp/records/169469
https://ipsj.ixsq.nii.ac.jp/records/169469
c6d15a9f-7b93-41d0-9d12-089c1aedead3
名前 / ファイル ライセンス アクション
IPSJ-JNL5707002.pdf IPSJ-JNL5707002.pdf (1.4 MB)
Copyright (c) 2016 by the Information Processing Society of Japan
オープンアクセス
Item type Journal(1)
公開日 2016-07-15
タイトル
タイトル Pain Level Detection From Facial Image Captured by Smartphone
タイトル
言語 en
タイトル Pain Level Detection From Facial Image Captured by Smartphone
言語
言語 eng
キーワード
主題Scheme Other
主題 [特集:Applications and the Internet in Conjunction with Main Topics of COMPSAC 2015(招待論文)] pain level detection, remote monitoring, quality of life, cancer patient's pain level.
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者所属
Marquette University
著者所属
Marquette University
著者所属
Marquette University
著者所属
IBCRF
著者所属
Amader Gram
著者所属(英)
en
Marquette University
著者所属(英)
en
Marquette University
著者所属(英)
en
Marquette University
著者所属(英)
en
IBCRF
著者所属(英)
en
Amader Gram
著者名 Md, Kamrul Hasan

× Md, Kamrul Hasan

Md, Kamrul Hasan

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Golam, Mushih Tanimul Ahsan

× Golam, Mushih Tanimul Ahsan

Golam, Mushih Tanimul Ahsan

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Sheikh, Iqbal Ahamed

× Sheikh, Iqbal Ahamed

Sheikh, Iqbal Ahamed

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Rechard, Love

× Rechard, Love

Rechard, Love

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Reza, Salim

× Reza, Salim

Reza, Salim

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著者名(英) Md, Kamrul Hasan

× Md, Kamrul Hasan

en Md, Kamrul Hasan

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Golam, Mushih Tanimul Ahsan

× Golam, Mushih Tanimul Ahsan

en Golam, Mushih Tanimul Ahsan

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Sheikh, Iqbal Ahamed

× Sheikh, Iqbal Ahamed

en Sheikh, Iqbal Ahamed

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Rechard, Love

× Rechard, Love

en Rechard, Love

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Reza, Salim

× Reza, Salim

en Reza, Salim

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論文抄録
内容記述タイプ Other
内容記述 Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.24(2016) No.4 (online)
DOI http://dx.doi.org/10.2197/ipsjjip.24.598
------------------------------
論文抄録(英)
内容記述タイプ Other
内容記述 Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.24(2016) No.4 (online)
DOI http://dx.doi.org/10.2197/ipsjjip.24.598
------------------------------
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AN00116647
書誌情報 情報処理学会論文誌

巻 57, 号 7, 発行日 2016-07-15
ISSN
収録物識別子タイプ ISSN
収録物識別子 1882-7764
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