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|a 10.1109/TPAMI.2022.3158902
|2 doi
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|a pubmed24n1129.xml
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|a eng
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| 100 |
1 |
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|a Clever, Henry M
|e verfasserin
|4 aut
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| 245 |
1 |
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|a BodyPressure - Inferring Body Pose and Contact Pressure From a Depth Image
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 06.04.2023
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|a Date Revised 05.05.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Contact pressure between the human body and its surroundings has important implications. For example, it plays a role in comfort, safety, posture, and health. We present a method that infers contact pressure between a human body and a mattress from a depth image. Specifically, we focus on using a depth image from a downward facing camera to infer pressure on a body at rest in bed occluded by bedding, which is directly applicable to the prevention of pressure injuries in healthcare. Our approach involves augmenting a real dataset with synthetic data generated via a soft-body physics simulation of a human body, a mattress, a pressure sensing mat, and a blanket. We introduce a novel deep network that we trained on an augmented dataset and evaluated with real data. The network contains an embedded human body mesh model and uses a white-box model of depth and pressure image generation. Our network successfully infers body pose, outperforming prior work. It also infers contact pressure across a 3D mesh model of the human body, which is a novel capability, and does so in the presence of occlusion from blankets
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| 650 |
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Grady, Patrick L
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Turk, Greg
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kemp, Charles C
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 1 vom: 28. Jan., Seite 137-153
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|g volume:45
|g year:2023
|g number:1
|g day:28
|g month:01
|g pages:137-153
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|u http://dx.doi.org/10.1109/TPAMI.2022.3158902
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