Early detection of depressive episodes is crucial in managing mental health disorders such as Major Depressive Disorder (MDD) and Bipolar Disorder. However, existing methods often necessitate active participation or are confined to clinical settings. Addressing this gap, we introduce PupilSense, a novel, deep learning-driven mobile system designed to discreetly track pupillary responses as users interact with their smartphones in their daily lives. This study presents a proof-of-concept exploration of PupilSense's capabilities, where we captured real-time pupillary data from users in naturalistic settings. Our findings indicate that PupilSense can effectively and passively monitor indicators of depressive episodes, offering a promising tool for continuous mental health assessment outside laboratory environments. This advancement heralds a significant step in leveraging ubiquitous mobile technology for proactive mental health care, potentially transforming how depressive episodes are detected and managed in everyday contexts.

翻译：抑郁发作的早期检测对于管理重度抑郁症和双相障碍等精神健康疾病至关重要。然而，现有方法通常需要主动参与或局限于临床环境。针对这一空白，我们提出PupilSense——一种基于深度学习的新型移动系统，能够在用户日常使用智能手机时隐蔽追踪其瞳孔反应。本研究展示了PupilSense能力的概念验证探索，我们在自然环境中捕获了用户的实时瞳孔数据。结果表明，PupilSense可以有效且被动地监测抑郁发作的指标，为实验室环境之外的持续性精神健康评估提供了一种有前景的工具。这一进展标志着利用普适移动技术实现主动精神健康护理的重要一步，有望改变日常情境下抑郁发作的检测与管理方式。