Human pose estimation, the process of identifying joint positions in a
person's body from images or videos, represents a widely utilized technology
across diverse fields, including healthcare. One such healthcare application
involves in-bed pose estimation, where the body pose of an individual lying
under a blanket is analyzed. This task, for instance, can be used to monitor a
person's sleep behavior and detect symptoms early for potential disease
diagnosis in homes and hospitals. Several studies have utilized unimodal and
multimodal methods to estimate in-bed human poses. The unimodal studies
generally employ RGB images, whereas the multimodal studies use modalities
including RGB, long-wavelength infrared, pressure map, and depth map.
Multimodal studies have the advantage of using modalities in addition to RGB
that might capture information useful to cope with occlusions. Moreover, some
multimodal studies exclude RGB and, this way, better suit privacy preservation.
To expedite advancements in this domain, we conduct a review of existing
datasets and approaches. Our objectives are to show the limitations of the
previous studies, current challenges, and provide insights for future works on
the in-bed human pose estimation field.

对现有数据集和方法进行评审，旨在展示卧床人体姿势估计领域以往研究的局限性、当前挑战，并为未来工作提供指导性见解。

卧姿估计综述

In-Bed Pose Estimation: A Review

Human in-bed pose estimation has huge practical values in medical and
healthcare applications yet still mainly relies on expensive pressure mapping
(PM) solutions. In this paper, we introduce our novel physics inspired
vision-based approach that addresses the challenging issues associated with the
in-bed pose estimation problem including monitoring a fully covered person in
complete darkness. We reformulated this problem using our proposed Under the
Cover Imaging via Thermal Diffusion (UCITD) method to capture the high
resolution pose information of the body even when it is fully covered by using
a long wavelength IR technique. We proposed a physical hyperparameter concept
through which we achieved high quality groundtruth pose labels in different
modalities. A fully annotated in-bed pose dataset called
Simultaneously-collected multimodal Lying Pose (SLP) is also formed/released
with the same order of magnitude as most existing large-scale human pose
datasets to support complex models' training and evaluation. A network trained
from scratch on it and tested on two diverse settings, one in a living room and
the other in a hospital room showed pose estimation performance of 99.5% and
95.7% in PCK0.2 standard, respectively. Moreover, in a multi-factor comparison
with a state-of-the art in-bed pose monitoring solution based on PM, our
solution showed significant superiority in all practical aspects by being 60
times cheaper, 300 times smaller, while having higher pose recognition
granularity and accuracy.

提出了一种基于 UCITD 方法的姿态识别技术，该技术通过长波长红外线技术对人体进行高分辨率的姿态信息捕捉，形成了一个完全标注的姿态数据集同时也证明其在医疗和医疗保健应用中具有巨大的实用价值。

透视覆盖物下：一种基于物理引导学习的床上姿势估计方法

Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed  Pose Estimation

Although human pose estimation for various computer vision (CV) applications
has been studied extensively in the last few decades, yet in-bed pose
estimation using camera-based vision methods has been ignored by the CV
community because it is assumed to be identical to the general purpose pose
estimation methods. However, in-bed pose estimation has its own specialized
aspects and comes with specific challenges including the notable differences in
lighting conditions throughout a day and also having different pose
distribution from the common human surveillance viewpoint. In this paper, we
demonstrate that these challenges significantly lessen the effectiveness of
existing general purpose pose estimation models. In order to address the
lighting variation challenge, infrared selective (IRS) image acquisition
technique is proposed to provide uniform quality data under various lighting
conditions. In addition, to deal with unconventional pose perspective, a 2-end
histogram of oriented gradient (HOG) rectification method is presented. In this
work, we explored the idea of employing a pre-trained convolutional neural
network (CNN) model trained on large public datasets of general human poses and
fine-tuning the model using our own shallow in-bed IRS dataset. We developed an
IRS imaging system and collected IRS image data from several realistic
life-size mannequins in a simulated hospital room environment. A pre-trained
CNN called convolutional pose machine (CPM) was repurposed for in-bed pose
estimation by fine-tuning its specific intermediate layers. Using the HOG
rectification method, the pose estimation performance of CPM significantly
improved by 26.4% in PCK0.1 criteria compared to the model without such
rectification.

本文介绍了利用卷积神经网络、红外图像采集技术和方向梯度直方图矫正方法解决特殊环境下的床上人体姿势估计的问题。通过特定数据集和网络层有针对性地微调预训练的卷积姿态机器模型，使用 HOG 方法改善了模型的表现。