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.

翻译：人体姿态估计是指从图像或视频中识别人体关节位置的技术，广泛应用于医疗保健等多个领域。其中一项医疗应用是床上人体姿态估计，即分析躺在毯子下的人的体态。该任务可用于监测人的睡眠行为，并在家庭和医院环境中早期发现潜在疾病症状。现有研究采用单模态和多模态方法进行床上人体姿态估计。单模态研究通常使用RGB图像，而多模态研究则结合RGB、长波红外、压力图和深度图等模态。多模态研究可利用RGB之外的其他模态捕捉有助于应对遮挡的信息，具有显著优势。此外，部分多模态研究排除RGB，从而更好地保护隐私。为促进该领域发展，我们对现有数据集和方法进行了综述，旨在揭示前人研究的局限性、当前面临的挑战，并为未来床上人体姿态估计研究提供见解。