MoodCam introduces a novel method for assessing mood by utilizing facial affect analysis through the front-facing camera of smartphones during everyday activities. We collected facial behavior primitives during 15,995 real-world phone interactions involving 25 participants over four weeks. We developed three models for timely intervention: momentary, daily average, and next day average. Notably, our models exhibit AUC scores ranging from 0.58 to 0.64 for Valence and 0.60 to 0.63 for Arousal. These scores are comparable to or better than those from some previous studies. This predictive ability suggests that MoodCam can effectively forecast mood trends, providing valuable insights for timely interventions and resource planning in mental health management. The results are promising as they demonstrate the viability of using real-time and predictive mood analysis to aid in mental health interventions and potentially offer preemptive support during critical periods identified through mood trend shifts.

翻译：MoodCam提出了一种通过智能手机前置摄像头在日常活动中进行面部情感分析来评估情绪的新方法。我们在四周时间内收集了25名参与者进行15,995次真实手机交互时的面部行为基元。我们开发了三种及时干预模型：瞬时模型、日均模型和次日平均模型。值得注意的是，我们的模型在效价维度上AUC分数范围为0.58至0.64，在唤醒维度上为0.60至0.63。这些分数与先前部分研究结果相当或更优。该预测能力表明MoodCam能有效预测情绪趋势，为心理健康管理中的及时干预和资源规划提供有价值的见解。研究结果具有前景，证明了利用实时和预测性情绪分析辅助心理健康干预的可行性，并有望在通过情绪趋势变化识别的关键时期提供先发性支持。