Human transports in hospitals are labor-intensive and primarily performed in beds to save time. This transfer method does not promote the mobility or autonomy of the patient. To relieve the caregivers from this time-consuming task, a mobile robot is developed to autonomously transport humans around the hospital. It provides different transfer modes including walking and sitting in a wheelchair. The problem that this paper focuses on is to detect emergencies and ensure the well-being of the patient during the transport. For this purpose, the patient is tracked and monitored with a camera system. OpenPose is used for Human Pose Estimation and a trained classifier for emergency detection. We collected and published a dataset of 18,000 images in lab and hospital environments. It differs from related work because we have a moving robot with different transfer modes in a highly dynamic environment with multiple people in the scene using only RGB-D data. To improve the critical recall metric, we apply threshold moving and a time delay. We compare different models with an AutoML approach. This paper shows that emergencies while walking are best detected by a SVM with a recall of 95.8% on single frames. In the case of sitting transport, the best model achieves a recall of 62.2%. The contribution is to establish a baseline on this new dataset and to provide a proof of concept for the human emergency detection in this use case.

翻译：医院内的人类转运是一项劳动密集型工作，主要采用病床方式以节省时间。这种转运方法未能促进患者的行动能力或自主性。为减轻护理人员这一耗时任务，我们开发了一款移动机器人，可自主在医院内转运人类。它提供包括行走和轮椅乘坐在内的不同转运模式。本文聚焦的问题是在转运过程中检测紧急情况并确保患者健康。为此，我们使用摄像头系统对患者进行跟踪和监控。采用OpenPose进行人体姿态估计，并使用训练好的分类器进行紧急情况检测。我们收集并公开了一个包含18000张实验室和医院环境图像的数据库。本研究与相关工作的区别在于，我们拥有一台移动机器人，具备不同的转运模式，能够在仅使用RGB-D数据的高动态多人物场景中运行。为优化关键的召回率指标，我们应用了阈值移动和时间延迟技术。通过AutoML方法比较了不同模型。本文表明，在行走转运模式下，支持向量机（SVM）在单帧上的召回率最高达95.8%。在坐姿转运情况下，最佳模型的召回率为62.2%。本研究的贡献在于为这一新数据集建立了基准，并为该应用场景的人类紧急情况检测提供了概念验证。