用眼动和头部动作区分临床训练水平，准确率超85%。
Assessing Medical Training Skills via Eye and Head Movements
- 通过眼动与头部运动数据捕捉临床技能差异
- 头部特征识别准确率达F1=0.85，AUC=0.86
- 适合用于无感化医疗培训评估，可搭配传统评分
我们通过分析眼动与头部运动，研究临床环境中技能发展的规律。24名医务人员参与模拟新生儿分娩训练。计算了瞳孔反应率、注视持续时间及角速度等关键指标。结果表明，眼动与头部追踪能有效区分有经验与无经验的医护人员，尤其在产程任务中表现显著。头部相关特征的F1得分为0.85，AUC为0.86；瞳孔相关特征的F1得分为0.77，AUC为0.85。研究为基于消费级眼动眼镜的隐式技能评估模型提供了基础，可作为主观评分等传统方法的补充工具。
原文摘要 · Abstract (English)
We examined eye and head movements to gain insights into skill development in clinical settings. A total of 24 practitioners participated in simulated baby delivery training sessions. We calculated key metrics, including pupillary response rate, fixation duration, or angular velocity. Our findings indicate that eye and head tracking can effectively differentiate between trained and untrained practitioners, particularly during labor tasks. For example, head-related features achieved an F1 score of 0.85 and AUC of 0.86, whereas pupil-related features achieved F1 score of 0.77 and AUC of 0.85. The results lay the groundwork for computational models that support implicit skill assessment and training in clinical settings by using commodity eye-tracking glasses as a complementary device to more traditional evaluation methods such as subjective scores.
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