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

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 n...

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Bibliographic Details
Published in2024 International Conference on Activity and Behavior Computing (ABC) pp. 01 - 13
Main Authors Islam, Rahul, Bae, Sang Won
Format Conference Proceeding
LanguageEnglish
Published IEEE 29.05.2024
Subjects
Affective computing
Depression
Laboratories
Machine Learning
Mental health
Monitoring
Pupillometry
Real-time systems
Smart phones
Wearable devices
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Summary: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.
DOI:10.1109/ABC61795.2024.10652166