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.

该研究提出了一种基于深度图像推断人体与床垫之间接触压力的方法，从而预防医疗卫生中的压力伤害，该方法通过添加人体软体物理模拟合成数据进行训练，并应用了新颖的深度网络模型成功推断人体姿态，并能够在被毯子遮挡的情况下推断人体 3D 网格模型的接触压力。