BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image
This work addresses pressure injury prevention in healthcare by enabling non-invasive monitoring of body pose and contact pressure in occluded scenarios.
The paper tackles the problem of inferring body pose and contact pressure from a depth image, specifically for a person in bed occluded by bedding, using a novel deep network trained on augmented synthetic and real data, achieving improved pose estimation and novel pressure inference.
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.