自主医院转运过程中的人体紧急情况检测
Human Emergency Detection during Autonomous Hospital Transports
AI总结：
本文针对医院自主转运机器人场景，提出基于OpenPose姿态估计和分类器的人体紧急情况检测方法，并发布包含18,000张图像的新数据集，通过阈值移动和AutoML比较模型，在步行和轮椅模式下分别实现95.8%和62.2%的召回率，为该任务建立基线。
摘要：
医院中的人员转运属于劳动密集型工作，且主要在床上进行以节省时间。这种转运方式不利于患者的移动能力或自主性。为将护理人员从这项耗时任务中解放出来，我们开发了一种移动机器人，可在医院内自主转运人员。该机器人提供不同的转运模式，包括步行和乘坐轮椅。本文关注的问题是在转运过程中检测紧急情况并确保患者的健康。为此，通过摄像系统对患者进行跟踪和监测。使用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。我们收集并发布了一个包含18,000张图像的数据集，这些图像来自实验室和医院环境。该数据集与相关工作不同，因为我们的机器人处于移动状态，具有不同的转运模式，且场景为高度动态环境，画面中有多人，仅使用RGB-D数据。为提高关键的召回率指标，我们应用了阈值移动和时间延迟方法。我们使用AutoML方法比较了不同模型。本文表明，步行过程中的紧急情况由SVM检测效果最佳，在单帧上的召回率达到95.8%。在乘坐轮椅转运的情况下，最佳模型实现了62.2%的召回率。本文的贡献在于为这一新数据集建立基线，并为该用例中的人体紧急情况检测提供概念验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
