{
  "id": "2105.09936",
  "version": "v1",
  "published": "2021-05-20T17:55:31.000Z",
  "updated": "2021-05-20T17:55:31.000Z",
  "title": "BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image",
  "authors": [
    "Henry M. Clever",
    "Patrick Grady",
    "Greg Turk",
    "Charles C. Kemp"
  ],
  "comment": "19 pages, 11 figures, 4 tables",
  "categories": [
    "cs.CV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-05-20T17:55:31.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "depth image",
      "inferring body pose",
      "successfully infers body pose",
      "infers contact pressure",
      "embedded human body mesh model"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 19,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}