Infant pose monitoring during sleep has multiple applications in both healthcare and home settings. In a healthcare setting, pose detection can be used for region of interest detection and movement detection for noncontact based monitoring systems. In a home setting, pose detection can be used to detect sleep positions which has shown to have a strong influence on multiple health factors. However, pose monitoring during sleep is challenging due to heavy occlusions from blanket coverings and low lighting. To address this, we present a novel dataset, Simultaneously-collected multimodal Mannequin Lying pose (SMaL) dataset, for under the cover infant pose estimation. We collect depth and pressure imagery of an infant mannequin in different poses under various cover conditions. We successfully infer full body pose under the cover by training state-of-art pose estimation methods and leveraging existing multimodal adult pose datasets for transfer learning. We demonstrate a hierarchical pretraining strategy for transformer-based models to significantly improve performance on our dataset. Our best performing model was able to detect joints under the cover within 25mm 86% of the time with an overall mean error of 16.9mm. Data, code and models publicly available at https://github.com/DanielKyr/SMaL

翻译：睡眠中的婴儿姿态监测在医疗和家庭环境中具有多种应用。在医疗场景中，姿态检测可用于非接触式监测系统中的感兴趣区域检测和运动检测；在家庭场景中，姿态检测可用于识别睡眠姿势，而睡眠姿势已被证明对多种健康因素有显著影响。然而，由于毛毯遮挡和低光照条件导致的严重遮挡，睡眠时的姿态监测极具挑战性。为解决这一问题，我们提出了一种新型数据集——同步采集的多模态人体模型躺姿（SMaL）数据集，用于被子遮挡下的婴儿姿态估计。我们采集了婴儿人体模型在不同遮挡条件下摆出多种姿态的深度图像和压力图像。通过训练先进姿态估计方法，并利用现有成人多模态姿态数据集进行迁移学习，我们成功推断出被子遮挡下的全身姿态。我们展示了一种针对Transformer模型的层次化预训练策略，显著提升了在该数据集上的性能。我们的最佳模型在86%的情况下能够检测到被子遮挡下的关节，误差在25毫米以内，整体平均误差为16.9毫米。数据、代码和模型公开于：https://github.com/DanielKyr/SMaL