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使用基于视频的眼睛和面部特征进行个性化的警觉预测

Manivannan Subramaniyan^1,2, Francisco G Vital-Lopez^1,2, Tracy J Doty³

¹Department of Defense Biotechnology High Performance Computing Software Applications Institute, Defense Health Agency Research & Development, Medical Research and Development Command, Fort Detrick, MD, USA.

|June 3, 2025

概括

面部视频现在可以预测警觉障碍,为耗时的精神运动警觉测试 (PVT) 提供了一个实用的替代方案. 这种方法允许在不中断日常活动的情况下进行个性化的警觉预测.

科学领域:

神经科学是一个神经科学.
人类表现的人类表现.
生物医学工程生物医学工程

背景情况:

心理运动警觉测试 (PVT) 是评估警觉障碍的标准,但由于其耗时性质,对于现实世界的使用是不切实际的.
现有的警觉监测方法通常需要积极参与,这限制了它们在日常生活中的适用性.

研究的目的:

开发一种非侵入性方法,通过被动记录的面部视频来预测个体警觉障碍.
建立个性化的警觉-损伤预测,作为传统PVT评估的切实替代方案.

主要方法:

从26名参与者进行62小时的完全睡眠剥夺的视频录制中提取了面部和眼睛的特征.
线性混合效应模型使用这些面部特征进行训练,以预测PVT性能 (平均反应时间).
通过将预测的PVT数据集成到统一的绩效模型 (UMP) 中,生成了个性化的警觉-损伤预测.

主要成果:

这些模型实现了39毫秒的平均根平均平方误差 (RMSE) 来从面部特征预测PVT平均反应时间.
基于视频衍生PVT估计的个性化UMP预测显示,平均RMSE为36毫秒,与参与者内休息条件 (30毫秒) 的变化密切匹配.

结论:

开发了一种实用的,原理证明的方法,通过被动记录的面部视频来预测个体警觉障碍.
这种方法提供了一种可行的,不破坏性的方法来监测和预测现实环境中的警觉性变化.

关键词:

警性警性警性警性警性眼睛眼睛眼睛眼数学模型是一个数学模型.心理运动警觉测试测试睡眠损失睡眠损失睡眠损失视频录制录制视频录制

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