MoodPupilar：在自然场景下通过智能手机检测的瞳孔反应预测情绪
MoodPupilar: Predicting Mood Through Smartphone Detected Pupillary Responses in Naturalistic Settings
AI总结：
本文提出MoodPupilar，利用智能手机日常使用时捕捉的瞳孔反应预测每日情绪；基于25人四周数据和GLOBEM平台评估，其MCC在效价和唤醒度上分别达0.15和0.12。
摘要：
MoodPupilar提出了一种新的情绪评估方法，该方法利用智能手机前置摄像头在日常使用过程中捕捉到的瞳孔反应。在为期四周的时间里，研究人员从25名参与者处收集数据，以开发能够预测每日平均情绪的模型。借助GLOBEM行为建模平台，我们对瞳孔反应作为情绪预测因子的效用进行了基准测试。我们提出的模型在效价上取得了0.15的Matthews相关系数（MCC）分数，在唤醒度上取得了0.12分，与GLOBEM支持的现有行为建模算法所取得的结果相当甚至更优。这种准确预测情绪趋势的能力凸显了瞳孔反应数据的有效性，可为及时的心理健康干预和资源分配提供关键洞见。结果令人鼓舞，证明了实时且具有预测性的情绪分析在支持心理健康干预方面的潜力。
英文摘要：
MoodPupilar introduces a novel method for mood evaluation using pupillary response captured by a smartphone's front-facing camera during daily use. Over a four-week period, data was gathered from 25 participants to develop models capable of predicting daily mood averages. Utilizing the GLOBEM behavior modeling platform, we benchmarked the utility of pupillary response as a predictor for mood. Our proposed model demonstrated a Matthew's Correlation Coefficient (MCC) score of 0.15 for Valence and 0.12 for Arousal, which is on par with or exceeds those achieved by existing behavioral modeling algorithms supported by GLOBEM. This capability to accurately predict mood trends underscores the effectiveness of pupillary response data in providing crucial insights for timely mental health interventions and resource allocation. The outcomes are encouraging, demonstrating the potential of real-time and predictive mood analysis to support mental health interventions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
