基于差分动作单元的可解释图注意力网络用于压力识别(StressGAT)
Explainable graph attention network for stress recognition (StressGAT) via differential action units
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中文总结 AI 辅助
研究针对压力识别中传统架构的不足，提出基于差分动作单元的可解释图注意力网络StressGAT，利用图建模捕捉面部动态实现个性化识别，在压力诱导队列实验中取得高准确率，并通过多实例学习揭示压力区间和表情特征，为情感监测提供有力方案。
中文摘要 AI 辅助
压力是一个动态过程，面部表情存在显著个体差异。传统架构如循环神经网络和卷积神经网络常忽视个体基线或缺乏建模能力。本研究引入StressGAT，利用图建模关系归纳偏差捕捉面部动态。通过差分动作单元实现个性化识别。在58名参与者的压力诱导队列上，独立受试者留一受试者出(LOSO)交叉验证协议下准确率达88.62%。该架构还集成多实例学习注意力机制，为个性化情感监测提供了强大且可解释的解决方案。
英文摘要
Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), often overlook person-specific baselines or lack the representational capacity to model the non-linear temporal progression of distress due to sequential bottlenecks and rigid grid-based constraints. Furthermore, many deep learning models lack the interpretability required for clinical deployment. This study introduces StressGAT, a Graph Attention Network that leverages the relational inductive bias of graph modeling to capture complex facial dynamics that indicate acute stress. By using Differential Action Units, the framework normalizes individual responses relative to neutral baselines to achieve personalized recognition. The proposed model achieves 88.62\% accuracy on a diverse stress-induction cohort (58 participants) using a subject-independent, Leave-One-Subject-Out (LOSO) cross-validation protocol. Beyond predictive accuracy, the architecture integrates a Multiple Instance Learning (MIL) attention mechanism to identify peak stress intervals and reveal distinct expressivity phenotypes. By simultaneously optimizing for accuracy and interpretability, this framework provides a robust, explainable solution for personalized affective monitoring.
