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

• Kei, Iwase

• Yu, Enokibori

• Naoto, Yoshida

• Kenji, Mase

抄録

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 ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.29(2021) (online)DOI　http://dx.doi.org/10.2197/ipsjjip.29.620------------------------------

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 ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.29(2021) (online)DOI　http://dx.doi.org/10.2197/ipsjjip.29.620------------------------------

収録刊行物

• 情報処理学会論文誌

  情報処理学会論文誌 62 (10), 2021-10-15

キーワード

• [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

詳細情報

• CRID: 1050008290028163456
• NII論文ID: 170000185644
• NII書誌ID: AN00116647
• ISSN: 18827764
• Web Site: http://id.nii.ac.jp/1001/00213194/
• 本文言語コード: en
• 資料種別: journal article
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