In-bed Pressure-based Pose Estimation using Image Space Representation Learning

TL;DR

This paper introduces a novel end-to-end neural network framework that transforms ambiguous in-bed pressure data into image-like representations, enabling existing pose estimation models to accurately determine body positions for health monitoring and related applications.

Contribution

The paper presents a new method that preprocesses pressure data into image-like forms, improving pose estimation accuracy in in-bed pressure sensing systems.

Findings

High visual quality in generated pressure images

Improved pose estimation accuracy on public dataset

Effective reconstruction of unclear body parts

Abstract

Recent advances in deep pose estimation models have proven to be effective in a wide range of applications such as health monitoring, sports, animations, and robotics. However, pose estimation models fail to generalize when facing images acquired from in-bed pressure sensing systems. In this paper, we address this challenge by presenting a novel end-to-end framework capable of accurately locating body parts from vague pressure data. Our method exploits the idea of equipping an off-the-shelf pose estimator with a deep trainable neural network, which pre-processes and prepares the pressure data for subsequent pose estimation. Our model transforms the ambiguous pressure maps to images containing shapes and structures similar to the common input domain of the pre-existing pose estimation methods. As a result, we show that our model is able to reconstruct unclear body parts, which in turn enables pose estimators to accurately and robustly estimate the pose. We train and test our method on a manually annotated public pressure map dataset using a combination of loss functions. Results confirm the effectiveness of our method by the high visual quality in the generated images and the high pose estimation rates achieved.