  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.
