  Body weight, as an essential physiological trait, is of considerable
significance in many applications like body management, rehabilitation, and
drug dosing for patient-specific treatments. Previous works on the body weight
estimation task are mainly vision-based, using 2D/3D, depth, or infrared
images, facing problems in illumination, occlusions, and especially privacy
issues. The pressure mapping mattress is a non-invasive and privacy-preserving
tool to obtain the pressure distribution image over the bed surface, which
strongly correlates with the body weight of the lying person. To extract the
body weight from this image, we propose a deep learning-based model, including
a dual-branch network to extract the deep features and pose features
respectively. A contrastive learning module is also combined with the
deep-feature branch to help mine the mutual factors across different postures
of every single subject. The two groups of features are then concatenated for
the body weight regression task. To test the model's performance over different
hardware and posture settings, we create a pressure image dataset of 10
subjects and 23 postures, using a self-made pressure-sensing bedsheet. This
dataset, which is made public together with this paper, together with a public
dataset, are used for the validation. The results show that our model
outperforms the state-of-the-art algorithms over both 2 datasets. Our research
constitutes an important step toward fully automatic weight estimation in both
clinical and at-home practice. Our dataset is available for research purposes
at: https://github.com/USTCWzy/MassEstimation.
