用普通摄像头捕捉面部动态，实时判断人脑负荷高低。
Facial Movement Dynamics Reveal Workload During Complex Multitasking
- 通过分析面部关键点的运动速度、加速度等动态特征
- 模型在个体校准后准确率达73%，显著优于任务表现指标
- 适合需个性化校准的智能交互系统，不适用于跨人通用
实时认知负荷监测对安全关键场景至关重要，但现有方法存在侵入性强、成本高或时间分辨率不足等问题。本研究测试了标准网络摄像头采集的面部运动动态是否可作为低成本替代方案。72名参与者在不同负荷条件下完成多任务模拟（OpenMATB），使用OpenPose追踪面部关键点。提取线性运动学特征（速度、加速度、位移）和递归量化特征。随着负荷增加，运动幅度上升，时间结构先混乱后形成复杂模式，眼头协调性下降。基于姿态运动学的随机森林分类器准确率达85%（优于任务表现指标的55%），但跨被试泛化能力差（43% vs. 33%随机水平）。个体特异性模型仅需每条件2分钟校准，准确率持续提升至73%且未饱和。面部运动动态能敏感反映负荷变化，配合简单校准即可实现基于消费级摄像头的自适应界面，但个体差异限制跨人通用性。
原文摘要 · Abstract (English)
Real-time cognitive workload monitoring is crucial in safety-critical environments, yet established measures are intrusive, expensive, or lack temporal resolution. We tested whether facial movement dynamics from a standard webcam could provide a low-cost alternative. Seventy-two participants completed a multitasking simulation (OpenMATB) under varied load while facial keypoints were tracked via OpenPose. Linear kinematics (velocity, acceleration, displacement) and recurrence quantification features were extracted. Increasing load altered dynamics across timescales: movement magnitudes rose, temporal organisation fragmented then reorganised into complex patterns, and eye-head coordination weakened. Random forest classifiers trained on pose kinematics outperformed task performance metrics (85% vs. 55% accuracy) but generalised poorly across participants (43% vs. 33% chance). Participant-specific models reached 50% accuracy with minimal calibration (2 minutes per condition), improving continuously to 73% without plateau. Facial movement dynamics sensitively track workload with brief calibration, enabling adaptive interfaces using commodity cameras, though individual differences limit cross-participant generalisation.
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