Abstract
This study examines how Critical Care Air Transport Team (CCATT) members are trained using mixed-reality simulations that replicate the high-pressure conditions of aeromedical evacuation. Each team - a physician, nurse, and respiratory therapist - must stabilize severely injured soldiers by managing ventilators, IV pumps, and suction devices during flight. Proficient performance requires clinical expertise and cognitive skills, such as situational awareness, rapid decision-making, effective communication, and coordinated task management, all of which must be maintained under stress. Recent advances in simulation and multimodal data analytics enable more objective and comprehensive performance evaluation. In contrast, traditional instructor-led assessments are subjective and may overlook critical events, thereby limiting generalizability and consistency. However, AI-based automated and more objective evaluation metrics still demand human input to train the AI algorithms to assess complex team dynamics in the presence of environmental noise and the need for accurate re-identification in multi-person tracking. To address these challenges, we introduce a systematic, data-driven assessment framework that combines Cognitive Task Analysis (CTA) with Multimodal Learning Analytics (MMLA). We have developed a domain-specific CTA model for CCATT training and a vision-based action recognition pipeline using a fine-tuned Human-Object Interaction model, the Cascade Disentangling Network (CDN), to detect and track trainee-equipment interactions over time. These interactions automatically yield performance indicators (e.g., reaction time, task duration), which are mapped onto a hierarchical CTA model tailored to CCATT operations, enabling interpretable, domain-relevant performance evaluations.
Abstract (translated)
本研究探讨了如何利用混合现实模拟训练危重病航空转运团队（CCATT）成员。每个团队包括一名医生、一名护士和一名呼吸治疗师，他们必须在飞行过程中通过管理呼吸机、静脉输液泵以及吸引装置来稳定严重受伤的士兵的生命体征。熟练的表现需要临床专业知识和认知技能，如情境意识、快速决策能力、有效沟通及任务协调管理，并且这些都需在压力环境下维持。 近期模拟技术与多模态数据分析的进步使得更客观全面地评估性能成为可能。相比之下，传统的由教师指导的评估方式较为主观，并可能忽略关键事件，从而限制了一致性和通用性。然而，基于人工智能的自动化和更客观的评价指标仍然需要人类输入来训练AI算法，以评估复杂团队动态中的环境噪音以及多人追踪时准确识别的需求。 为应对这些挑战，我们引入了一个系统性的、数据驱动的评估框架，该框架结合了认知任务分析（CTA）与多模态学习分析（MMLA）。我们开发了一个针对CCATT训练领域的特定CTA模型，并使用了一种基于视觉的动作识别管道——精调的人机交互模型，即级联解构网络（CDN），来检测并跟踪受训者与设备之间的互动。这些互动自动产生了性能指标（例如反应时间、任务持续时间等），它们映射到了一个层次化的CTA模型中，该模型专为CCATT操作设计，从而能够进行解释性且领域相关的绩效评估。
URL
https://arxiv.org/abs/2509.17888
