Research Article
Anxiety Detection Leveraging Mobile Passive Sensing
@INPROCEEDINGS{10.1007/978-3-030-64991-3_15, author={Lionel M. Levine and Migyeong Gwak and Kimmo K\aa{}rkk\aa{}inen and Shayan Fazeli and Bita Zadeh and Tara Peris and Alexander S. Young and Majid Sarrafzadeh}, title={Anxiety Detection Leveraging Mobile Passive Sensing}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health. 15th EAI International Conference, BODYNETS 2020, Tallinn, Estonia, October 21, 2020, Proceedings}, proceedings_a={BODYNETS}, year={2020}, month={12}, keywords={Mobile application Anxiety Remote mental health monitoring Passive sensing Machine learning}, doi={10.1007/978-3-030-64991-3_15} }- Lionel M. Levine
Migyeong Gwak
Kimmo Kärkkäinen
Shayan Fazeli
Bita Zadeh
Tara Peris
Alexander S. Young
Majid Sarrafzadeh
Year: 2020
Anxiety Detection Leveraging Mobile Passive Sensing
BODYNETS
Springer
DOI: 10.1007/978-3-030-64991-3_15
Abstract
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.
