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.
翻译:人体及其周围的接触压力具有重要影响。 例如, 它在舒适、安全、姿势和健康方面起着作用。 我们提出一种方法, 推断人体和床垫之间从深度图像中接触压力。 具体地说, 我们侧重于使用从向下对面的摄像头的深度图像, 来推断卧床上休息时对身体的深度压力, 这直接适用于预防保健中的压力伤害。 我们的方法是用人体的软体物理模拟、 床垫、 压力感应垫和毯子生成的合成数据来增加一个真实的数据集。 我们引入了一个新型的深度网络, 我们用一个强化的数据集来培训, 用真实的数据进行评估。 网络包含一个嵌入的人体机身网格模型, 并使用一个深度和压力图像生成的白箱模型。 我们的网络成功地推断身体的构成, 优于先前的工作。 它还推断人体的3D网形模型的压力是新型能力, 在毯子的封闭处这样做。