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.

翻译：体重作为一种重要的生理特征，在身体管理、康复治疗以及患者特异性药物剂量调整等众多应用中具有显著意义。以往的体重估计工作主要基于视觉方法，使用2D/3D、深度或红外图像，面临光照、遮挡以及尤其是隐私问题。压力映射床垫是一种非侵入性且保护隐私的工具，用于获取床面压力分布图像，该图像与躺卧者的体重强相关。为了从该图像中提取体重，我们提出了一种基于深度学习的模型，包含一个双分支网络，分别用于提取深层特征和姿态特征。对比学习模块也与深层特征分支结合，以帮助挖掘每个受试者不同姿态之间的共同因素。随后将两组特征拼接用于体重回归任务。为测试模型在不同硬件和姿态设置下的性能，我们使用自制的压力传感床单创建了一个包含10名受试者、23种姿态的压力图像数据集。该数据集随本文公开，并与一个公开数据集一起用于验证。结果表明，我们的模型在两个数据集上均优于现有最优算法。本研究向着临床和家庭场景下的全自动体重估计迈出了重要一步。我们的数据集可在以下研究用途地址获取：https://github.com/USTCWzy/MassEstimation。