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.

本文提出了一种基于移动传感器数据来检测罕见生活事件的多任务框架，通过使用格兰杰因果性和无监督自编码器以及辅助序列预测器来提高检测准确性，结果显示该方法的 F1 值达到了 0.34，表现优于多个基线，结果为罕见事件检测提供了有效的方法。

基于多任务学习使用移动感知技术进行罕见生活事件检测

Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning

Emotion prediction plays an essential role in mental health and emotion-aware
computing. The complex nature of emotion resulting from its dependency on a
person's physiological health, mental state, and his surroundings makes its
prediction a challenging task. In this work, we utilize mobile sensing data to
predict happiness and stress. In addition to a person's physiological features,
we also incorporate the environment's impact through weather and social
network. To this end, we leverage phone data to construct social networks and
develop a machine learning architecture that aggregates information from
multiple users of the graph network and integrates it with the temporal
dynamics of data to predict emotion for all the users. The construction of
social networks does not incur additional cost in terms of EMAs or data
collection from users and doesn't raise privacy concerns. We propose an
architecture that automates the integration of a user's social network affect
prediction, is capable of dealing with the dynamic distribution of real-life
social networks, making it scalable to large-scale networks. Our extensive
evaluation highlights the improvement provided by the integration of social
networks. We further investigate the impact of graph topology on model's
performance.

通过手机感应数据和社交网络构建及机器学习算法综合分析多个用户和时序特征，利用社交网络对情感进行预测。