Estimating Pose from Pressure Data for Smart Beds with Deep Image-based Pose Estimators
This work addresses pose estimation for applications like hospital patient monitoring and smart homes, but it is incremental as it adapts existing methods to pressure data.
The paper tackled the problem of estimating body pose from ambiguous pressure data in smart beds by exploring strategies that combine learnable pre-processing with re-training of pre-existing image-based pose estimators, achieving very high pose estimation accuracy.
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.