EgoEMS：面向紧急医疗服务认知辅助的高保真多模态第一视角数据集
EgoEMS: A High-Fidelity Multimodal Egocentric Dataset for Cognitive Assistance in Emergency Medical Services
AI总结：
针对急救人员高认知负荷问题，本文推出首个符合标准的高保真多模态第一视角EMS数据集EgoEMS，配套核心任务基准，支撑急救AI认知助手研发。
摘要：
紧急医疗服务（EMS）对急症患者的生存至关重要，但急救人员在高风险场景中往往面临极高的认知负荷。作为虚拟协作伙伴的AI认知助手可通过支持实时数据采集与决策来缓解这一负担。为实现这一目标，我们推出EgoEMS，这是首个端到端高保真多模态多人数据集，由62名参与者（含46名EMS专业人员）完成233个模拟急救场景，从第一视角采集了超20小时符合操作流程的真实EMS活动数据。该数据集联合EMS专家开发、符合国家标准，采用开源、低成本、可复现的数据采集系统构建，标注内容包含关键步骤、带说话人分离的时间戳音频转写、动作质量指标、带分割掩码的边界框。为突出真实性，数据集纳入了反映真实急救动态的急救人员-患者交互内容。我们还提供了一套面向实时多模态关键步骤识别与动作质量评估的基准，这些任务是开发EMS AI辅助工具的核心。我们希望EgoEMS能推动研究界拓展智能EMS系统的边界，最终助力改善患者预后。
英文摘要：
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.
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。
