Joint Position Estimation for Body Pressure Images during Sleep: An Extension for CPM Using Body Area and Posture Estimation Mashups

• Item type: Journal(1)
• 公開日: 2021-10-15
• タイトル: Joint Position Estimation for Body Pressure Images during Sleep: An Extension for CPM Using Body Area and Posture Estimation Mashups
• タイトル / 言語: en
• タイトル: Joint Position Estimation for Body Pressure Images during Sleep: An Extension for CPM Using Body Area and Posture Estimation Mashups
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：ユビキタスコンピューティングシステム（X）] sleeping posture, body pressure image, joint position estimation, body area estimation, posture classification
• 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: Graduate School of Informatics, Nagoya University
• 著者所属: Graduate School of Informatics, Nagoya University
• 著者所属: Institute of Innovation for Future Society, Nagoya University
• 著者所属: Graduate School of Informatics, Nagoya University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Nagoya University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Nagoya University
• 著者所属(英): en
• 著者所属(英): Institute of Innovation for Future Society, Nagoya University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Nagoya University
• 著者名: Kei, Iwase; Yu, Enokibori; Naoto, Yoshida; Kenji, Mase
• 著者名(英): Kei, Iwase; Yu, Enokibori; Naoto, Yoshida; Kenji, Mase
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Estimating sleeping postures with body joint positions is critical for identifying potential sleeping problems and the risk of pressure ulcers. Many methods have estimated postures with body joint positions from camera images for general purposes. However, visual monitoring of sleeping contexts suffers from privacy and occlusion issues due to blankets, pillows, etc. An approach to solve those issues is the use of body pressure images obtained from bed surfaces. We have developed a textile-based sheet-type pressure sensor to avoid such issues. Unfortunately, its use raises other issues that are absent from camera images such as low resolution and noise caused by the wrinkling of sensor sheets. In this paper, we extend DNN-based joint estimation, called Convolutional Pose Machine (CPM), using body area and posture estimation mashups to improve the accuracy of joint estimation. The following are our evaluation results with cross-validation with 16 joints in six sleeping postures of 12 subjects: 7.15cm accuracy in mean absolute error (MAE), which is a 33.7% improvement from the standard CPM, and 8.52cm accuracy in MAE, which is a 37.4% improvement from CPM with camera images in situations using a pillow and a blanket.; ------------------------------; 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.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.620; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Estimating sleeping postures with body joint positions is critical for identifying potential sleeping problems and the risk of pressure ulcers. Many methods have estimated postures with body joint positions from camera images for general purposes. However, visual monitoring of sleeping contexts suffers from privacy and occlusion issues due to blankets, pillows, etc. An approach to solve those issues is the use of body pressure images obtained from bed surfaces. We have developed a textile-based sheet-type pressure sensor to avoid such issues. Unfortunately, its use raises other issues that are absent from camera images such as low resolution and noise caused by the wrinkling of sensor sheets. In this paper, we extend DNN-based joint estimation, called Convolutional Pose Machine (CPM), using body area and posture estimation mashups to improve the accuracy of joint estimation. The following are our evaluation results with cross-validation with 16 joints in six sleeping postures of 12 subjects: 7.15cm accuracy in mean absolute error (MAE), which is a 33.7% improvement from the standard CPM, and 8.52cm accuracy in MAE, which is a 37.4% improvement from CPM with camera images in situations using a pillow and a blanket.; ------------------------------; 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.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.620; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 62, 号 10, 発行日 2021-10-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764