Title:
3D Human Pose Estimation on a Configurable Bed from a Pressure Image
3D Human Pose Estimation on a Configurable Bed from a Pressure Image
Author(s)
Clever, Henry M.
Kapusta, Ariel
Park, Daehyung
Erickson, Zackory
Chitalia, Yash
Kemp, Charles C.
Kapusta, Ariel
Park, Daehyung
Erickson, Zackory
Chitalia, Yash
Kemp, Charles C.
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Abstract
Robots have the potential to assist people in bed,
such as in healthcare settings, yet bedding materials like sheets
and blankets can make observation of the human body difficult
for robots. A pressure-sensing mat on a bed can provide pressure
images that are relatively insensitive to bedding materials.
However, prior work on estimating human pose from pressure
images has been restricted to 2D pose estimates and flat beds.
In this work, we present two convolutional neural networks to
estimate the 3D joint positions of a person in a configurable
bed from a single pressure image. The first network directly
outputs 3D joint positions, while the second outputs a kinematic
model that includes estimated joint angles and limb lengths. We
evaluated our networks on data from 17 human participants
with two bed configurations: supine and seated. Our networks
achieved a mean joint position error of 77 mm when tested
with data from people outside the training set, outperforming
several baselines. We also present a simple mechanical model
that provides insight into ambiguity associated with limbs raised
off of the pressure mat, and demonstrate that Monte Carlo
dropout can be used to estimate pose confidence in these
situations. Finally, we provide a demonstration in which a
mobile manipulator uses our network’s estimated kinematic
model to reach a location on a person’s body in spite of the
person being seated in a bed and covered by a blanket.
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Date Issued
2018
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