Anxiety Detection Leveraging Mobile Passive Sensing

TL;DR

This paper introduces eWellness, a mobile app that passively collects sensor data to predict daily anxiety and depression levels, demonstrating promising results in real-time mental health monitoring.

Contribution

The study presents a novel passive sensing approach for anxiety detection using smartphones, with initial pilot results showing high prediction accuracy.

Findings

76% success rate in predicting daily anxiety and depression

Passive data collection is effective for mental health surveillance

eWellness enables real-time monitoring of anxiety levels

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.