用第一视角视频自动评估儿科重症医护团队领导力。
Leadership Assessment in Pediatric Intensive Care Unit Team Training
- 通过眼镜设备采集视频、眼神、对话等多模态数据，识别领导力行为线索。
- 实验发现专注时长、话语转换模式与直接指令频率显著关联领导力评分。
- 适合医疗培训评估、人机协作研究者参考，可推广至其他高危场景训练。
本文针对儿科重症监护室（PICU）团队的领导力评估问题，提出一种基于第一视角视觉的自动化分析框架。通过Aria眼镜设备采集医生在四场模拟训练中的一小时视频、音频、视线及头部运动数据，识别注视对象、眼神交流和对话模式等关键行为线索。采用REMoDNaV、SAM、YOLO与ChatGPT联合方法，实现注视目标检测、眼神接触识别与对话分类。实验显示，领导力评分与行为指标如注视时间、话语转换模式及直接指令频率存在显著相关性。结果表明，该数据采集与分析框架能有效支持对医护团队领导力的自动化评估。
原文摘要 · Abstract (English)
This paper addresses the task of assessing PICU team's leadership skills by developing an automated analysis framework based on egocentric vision. We identify key behavioral cues, including fixation object, eye contact, and conversation patterns, as essential indicators of leadership assessment. In order to capture these multimodal signals, we employ Aria Glasses to record egocentric video, audio, gaze, and head movement data. We collect one-hour videos of four simulated sessions involving doctors with different roles and levels. To automate data processing, we propose a method leveraging REMoDNaV, SAM, YOLO, and ChatGPT for fixation object detection, eye contact detection, and conversation classification. In the experiments, significant correlations are observed between leadership skills and behavioral metrics, i.e., the output of our proposed methods, such as fixation time, transition patterns, and direct orders in speech. These results indicate that our proposed data collection and analysis framework can effectively solve skill assessment for training PICU teams.
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