MoodPupilar: Predicting Mood Through Smartphone Detected Pupillary Responses in Naturalistic Settings

TL;DR

MoodPupilar presents a new smartphone-based method for predicting mood by analyzing pupillary responses captured during daily activities, showing promising results for mental health monitoring.

Contribution

The paper introduces a novel approach using smartphone pupillary data to predict mood, validated with models outperforming existing behavioral algorithms.

Findings

MCC score of 0.15 for Valence

MCC score of 0.12 for Arousal

Effective mood prediction in naturalistic settings

Abstract

MoodPupilar introduces a novel method for mood evaluation using pupillary response captured by a smartphone's front-facing camera during daily use. Over a four-week period, data was gathered from 25 participants to develop models capable of predicting daily mood averages. Utilizing the GLOBEM behavior modeling platform, we benchmarked the utility of pupillary response as a predictor for mood. Our proposed model demonstrated a Matthew's Correlation Coefficient (MCC) score of 0.15 for Valence and 0.12 for Arousal, which is on par with or exceeds those achieved by existing behavioral modeling algorithms supported by GLOBEM. This capability to accurately predict mood trends underscores the effectiveness of pupillary response data in providing crucial insights for timely mental health interventions and resource allocation. The outcomes are encouraging, demonstrating the potential of real-time and predictive mood analysis to support mental health interventions.