论文标题
焦虑检测利用移动被动传感
Anxiety Detection Leveraging Mobile Passive Sensing
论文作者
论文摘要
焦虑症是影响儿童和成人的最常见的精神病问题。但是，缺乏有效监控和管理焦虑的工具，并且对解决焦虑的独特挑战进行了相对有限的研究。从智能手机中利用被动和不引人注目的数据收集可能是经典方法的可行替代方法，可以实时心理健康监测和疾病管理。本文介绍了Ewellness，这是一种实验性移动应用程序，旨在以连续和被动的方式跟踪个人设备的全套传感器和用户log数据。我们报告了一项最初的试点研究，该研究在一个月的过程中跟踪了十个人，该研究仅根据被动监控的特征来预测每日焦虑和抑郁水平的成功率将近76％。
Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. However, tools to effectively monitor and manage anxiety are lacking, and comparatively limited research has been applied to addressing the unique challenges around anxiety. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods, allowing for real-time mental health surveillance and disease management. This paper presents eWellness, an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner. We report on an initial pilot study tracking ten people over the course of a month that showed a nearly 76% success rate at predicting daily anxiety and depression levels based solely on the passively monitored features.
