标题： 从手机数据、天气状况和个体特征中识别日常压力 显示英文标题

标题： Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

摘要： 研究表明，压力会降低生活质量并导致许多疾病。 因此，一些研究人员基于生理参数设计了压力检测系统。 然而，这些系统要求用户持续携带侵入式传感器。 在我们的论文中，我们提出了一种替代方法，提供了证据表明，可以通过行为指标可靠地识别日常压力，这些行为指标来自用户的手机活动以及额外的指标，如天气状况（环境暂时性属性的数据）和个性特征（关于个体永久性倾向的数据）。 我们的多因素统计模型是与人无关的，对于二类日常压力识别问题，其准确度得分为72.28%。 由于高度减少的低维特征空间（32d），该模型对于大多数多媒体应用来说易于实现。 此外，我们确定并讨论了具有强大预测能力的指标。 显示英文摘要

摘要： Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.