# Anxiety Detection Leveraging Mobile Passive Sensing

arXiv: https://arxiv.org/abs/2008.03810 · HTML: https://mlsanity.com/paper/2008.03810/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [A Self-supervised Framework for Improved Data-Driven Monitoring of Stress via Multi-modal Passive Sensing](https://mlsanity.com/paper/2303.14267/) (2023)

## Abstract (arXiv)

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.