Rare life events significantly impact mental health, and their detection in behavioral studies is a crucial step towards health-based interventions. We envision that mobile sensing data can be used to detect these anomalies. However, the human-centered nature of the problem, combined with the infrequency and uniqueness of these events makes it challenging for unsupervised machine learning methods. In this paper, we first investigate granger-causality between life events and human behavior using sensing data. Next, we propose a multi-task framework with an unsupervised autoencoder to capture irregular behavior, and an auxiliary sequence predictor that identifies transitions in workplace performance to contextualize events. We perform experiments using data from a mobile sensing study comprising N=126 information workers from multiple industries, spanning 10106 days with 198 rare events (<2%). Through personalized inference, we detect the exact day of a rare event with an F1 of 0.34, demonstrating that our method outperforms several baselines. Finally, we discuss the implications of our work from the context of real-world deployment.

翻译：罕见生活事件对心理健康有显著影响，在行为研究中检测此类事件是实现健康干预的关键步骤。我们设想移动感知数据可用于检测这些异常现象。然而，该问题以人为中心的特点，加上此类事件的低频性和独特性，使得无监督机器学习方法面临挑战。本文首先利用感知数据探究生活事件与人类行为之间的格兰杰因果关系。随后，我们提出一个多任务框架，包含用于捕捉异常行为的无监督自编码器，以及用于识别工作场所绩效变化以关联事件的辅助序列预测器。我们通过一项涵盖来自多个行业的126名信息工作者的移动感知研究数据进行实验，该研究跨越10106天，记录了198个罕见事件（占比<2%）。通过个性化推理，我们在准确检测罕见事件发生当天取得了0.34的F1分数，表明我们的方法优于多个基线模型。最后，我们从实际部署的角度讨论了本研究的应用意义。