Human Emergency Detection during Autonomous Hospital Transports

TL;DR

This paper presents a novel system for detecting human emergencies during autonomous hospital transports using a mobile robot, human pose estimation, and machine learning, achieving high recall rates in dynamic hospital environments.

Contribution

It introduces a new dataset and baseline for emergency detection during robot-assisted patient transport with multiple transfer modes.

Findings

SVM achieves 95.8% recall for walking emergencies.

Best model for sitting transport achieves 62.2% recall.

Threshold moving and delay improve critical recall metrics.

Abstract

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.