WEKO3
-
RootNode
アイテム
Pain Level Detection From Facial Image Captured by Smartphone
https://ipsj.ixsq.nii.ac.jp/records/169469
https://ipsj.ixsq.nii.ac.jp/records/169469c6d15a9f-7b93-41d0-9d12-089c1aedead3
名前 / ファイル | ライセンス | アクション |
---|---|---|
![]() |
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
× Golam, Mushih Tanimul Ahsan
× Sheikh, Iqbal Ahamed
× Rechard, Love
× Reza, Salim
|
|||||||||||||||
著者名(英) |
Md, Kamrul Hasan
× Md, Kamrul Hasan
× Golam, Mushih Tanimul Ahsan
× Sheikh, Iqbal Ahamed
× Rechard, Love
× Reza, Salim
|
|||||||||||||||
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 |