PupilSense: Detection of Depressive Episodes Through Pupillary Response in the Wild

TL;DR

PupilSense is a mobile system that passively monitors pupillary responses in real-world settings to detect depressive episodes, enabling continuous mental health assessment outside clinical environments.

Contribution

This paper introduces PupilSense, a novel deep learning-based mobile system for passive, real-time detection of depression indicators through pupillary response analysis in daily life.

Findings

Effective passive monitoring of pupillary responses in natural settings

Potential for early detection of depressive episodes

Advancement in mobile mental health assessment tools

Abstract

Early detection of depressive episodes is crucial in managing mental health disorders such as Major Depressive Disorder (MDD) and Bipolar Disorder. However, existing methods often necessitate active participation or are confined to clinical settings. Addressing this gap, we introduce PupilSense, a novel, deep learning-driven mobile system designed to discreetly track pupillary responses as users interact with their smartphones in their daily lives. This study presents a proof-of-concept exploration of PupilSense's capabilities, where we captured real-time pupillary data from users in naturalistic settings. Our findings indicate that PupilSense can effectively and passively monitor indicators of depressive episodes, offering a promising tool for continuous mental health assessment outside laboratory environments. This advancement heralds a significant step in leveraging ubiquitous mobile technology for proactive mental health care, potentially transforming how depressive episodes are detected and managed in everyday contexts.