发表机构
University of Trento; UiT the Arctic University of Norway（特伦托大学; 挪威北极大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建了基于真实手术室录制的扩展多模态手术团队互动数据集，含多级标注与反事实标注，支持团队协作建模及AI辅助协作系统设计，为高风险领域协作行为分析提供统一资源。
AI 中文摘要
在手术室等高风险环境中，对团队互动进行建模，对于理解协调、沟通及个体行为如何影响团队绩效与安全结果至关重要。该领域现有数据集常因模态、标注方案及格式分散，限制了其对现实协作过程进行综合分析的能力。本研究通过引入一个基于真实手术室录制内容构建的、用于手术团队互动分析的扩展多模态数据集，解决这一局限。从现有语料库出发，我们构建了可用于分析的数据版本，提供说话人 diarization（说话人分割）、转录文本及捕捉团队绩效、互动过程与个体特征的多级标注：团队绩效采用标准化手术团队协作评估协议进行评估，互动质量与个体属性则通过涵盖协作、团队动态及非技术技能的结构化评分方案标注。为进一步支持对协调故障与绩效变异性的研究，我们引入反事实标注，描述在观察到的互动失败下看似合理的替代团队结果，使分析特定行为模式如何与团队绩效的不同轨迹相关成为可能。此外，我们提供结构化的时间与关系表征，旨在支持对团队协作过程的计算建模及AI辅助协作系统的设计。该数据集旨在研究手术场景中个体行为、互动模式与团队层面过程如何共同影响团队结果，为高风险领域的协作行为分析提供统一资源。
英文摘要
Modeling team interactions in high-stakes environments such as operating rooms is critical for understanding how coordination, communication, and individual behaviors shape team performance and safety outcomes. Existing datasets in this domain are often fragmented across modalities, annotation schemes, and formats, limiting their ability to support integrated analyses of real-world collaborative processes. We address this limitation by introducing an extended multimodal dataset for surgical team interaction analysis, built from real operating room recordings. Starting from an existing corpus, we construct an analysis-ready version of the data by providing speaker diarization, transcripts, and multi-level annotations capturing team performance, interaction processes, and individual characteristics. Team performance is assessed using a standardized surgical teamwork evaluation protocol, while interaction quality and individual attributes are annotated through structured rating schemes covering collaboration, group dynamics, and non-technical skills. To further support the study of coordination breakdowns and performance variability, we introduce counterfactual annotations that describe plausible alternative team outcomes in the presence of observed interaction failures, enabling analysis of how specific behavioral patterns may relate to different trajectories of team performance. In addition, we provide structured temporal and relational representations designed to support computational modeling of teamwork processes and the design of AI-assisted collaborative systems. The dataset is designed to support the study of how individual actions, interaction patterns, and team-level processes jointly contribute to team outcomes in surgical settings, providing a unified resource for analyzing collaborative behavior in high-stakes domains.
CommentsAccepted at HCOMP 2026
