EgoEMS: A High-Fidelity Multimodal Egocentric Dataset for Cognitive Assistance in Emergency Medical Services

TL;DR

EgoEMS is a comprehensive, high-fidelity multimodal dataset capturing realistic emergency medical scenarios from an egocentric perspective, designed to facilitate AI development for cognitive assistance in EMS.

Contribution

This paper introduces EgoEMS, the first detailed multimodal dataset of EMS activities with annotations, benchmarks, and a low-cost, open-source data collection system.

Findings

EgoEMS includes over 20 hours of EMS activities from 62 participants.

Benchmarks for keystep recognition and action quality estimation are provided.

The dataset reflects real-world emergency dynamics with detailed annotations.

Abstract

Emergency Medical Services (EMS) are critical to patient survival in emergencies, but first responders often face intense cognitive demands in high-stakes situations. AI cognitive assistants, acting as virtual partners, have the potential to ease this burden by supporting real-time data collection and decision making. In pursuit of this vision, we introduce EgoEMS, the first end-to-end, high-fidelity, multimodal, multiperson dataset capturing over 20 hours of realistic, procedural EMS activities from an egocentric view in 233 simulated emergency scenarios performed by 62 participants, including 46 EMS professionals. Developed in collaboration with EMS experts and aligned with national standards, EgoEMS is captured using an open-source, low-cost, and replicable data collection system and is annotated with keysteps, timestamped audio transcripts with speaker diarization, action quality metrics, and bounding boxes with segmentation masks. Emphasizing realism, the dataset includes responder-patient interactions reflecting real-world emergency dynamics. We also present a suite of benchmarks for real-time multimodal keystep recognition and action quality estimation, essential for developing AI support tools for EMS. We hope EgoEMS inspires the research community to push the boundaries of intelligent EMS systems and ultimately contribute to improved patient outcomes.