Real-Time Assessment of Bystander Situation Awareness in Drone-Assisted First Aid
This work addresses a key gap in human-autonomy teaming for emergency response, enabling adaptive drone systems to guide untrained bystanders, though it is incremental as it builds on existing methods with a new dataset.
The paper tackled the problem of real-time assessment of bystander situational awareness in drone-assisted first aid for opioid overdose emergencies by introducing a simulation dataset and a video-based framework using graph embeddings and transformers, achieving a 9% improvement in Mean over Frames and 5% in Intersection over Union over a baseline.
Rapid naloxone delivery via drones offers a promising solution for responding to opioid overdose emergencies (OOEs), by extending lifesaving interventions to medically untrained bystanders before emergency medical services (EMS) arrive. Recognizing the critical role of bystander situational awareness (SA) in human-autonomy teaming (HAT), we address a key research gap in real-time SA assessment by introducing the Drone-Assisted Naloxone Delivery Simulation Dataset (DANDSD). This pioneering dataset captures HAT during simulated OOEs, where college students without medical training act as bystanders tasked with administering intranasal naloxone to a mock overdose victim. Leveraging this dataset, we propose a video-based real-time SA assessment framework that utilizes graph embeddings and transformer models to assess bystander SA in real time. Our approach integrates visual perception and comprehension cues--such as geometric, kinematic, and interaction graph features--and achieves high-performance SA prediction. It also demonstrates strong temporal segmentation accuracy, outperforming the FINCH baseline by 9% in Mean over Frames (MoF) and 5% in Intersection over Union (IoU). This work supports the development of adaptive drone systems capable of guiding bystanders effectively, ultimately improving emergency response outcomes and saving lives.