In-bed pose estimation has shown value in fields such as hospital patient monitoring, sleep studies, and smart homes. In this paper, we explore different strategies for detecting body pose from highly ambiguous pressure data, with the aid of pre-existing pose estimators. We examine the performance of pre-trained pose estimators by using them either directly or by re-training them on two pressure datasets. We also explore other strategies utilizing a learnable pre-processing domain adaptation step, which transforms the vague pressure maps to a representation closer to the expected input space of common purpose pose estimation modules. Accordingly, we used a fully convolutional network with multiple scales to provide the pose-specific characteristics of the pressure maps to the pre-trained pose estimation module. Our complete analysis of different approaches shows that the combination of learnable pre-processing module along with re-training pre-existing image-based pose estimators on the pressure data is able to overcome issues such as highly vague pressure points to achieve very high pose estimation accuracy.

床上姿势估计已在医院患者监测、睡眠研究和智能家居等领域显示出价值。在本文中，我们借助预先存在的姿势估计器探索了从高度模糊的压力数据中检测身体姿势的不同策略。我们通过直接使用它们或通过在两个压力数据集上重新训练它们来检查预训练姿势估计器的性能。我们还利用可学习的预处理域适应步骤探索了其他策略，该步骤将模糊的压力图转换为更接近通用姿势估计模块的预期输入空间的表示。因此，我们使用具有多个尺度的全卷积网络将压力图的姿势特定特征提供给预训练的姿势估计模块。