An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices

TL;DR

This study develops an explainable anomaly detection framework using wearable device data to monitor depression and anxiety symptom worsening, achieving high accuracy and interpretability for personalized mental health tracking.

Contribution

The paper introduces a novel LSTM autoencoder-based anomaly detection model with SHAP interpretability for mental health monitoring using consumer wearables.

Findings

Achieved an adjusted F1-score of 0.80 in detecting symptom-worsening episodes.

Resting heart rate was identified as the most influential feature in anomaly detection.

Higher detection performance for episodes with concurrent depression and anxiety escalation.

Abstract

Continuous monitoring of behavior and physiology via wearable devices offers a novel, objective method for the early detection of worsening depression and anxiety. In this study, we present an explainable anomaly detection framework that identifies clinically meaningful increases in symptom severity using consumer-grade wearable data. Leveraging data from 2,023 participants with defined healthy baselines, our LSTM autoencoder model learned normal health patterns of sleep duration, step count, and resting heart rate. Anomalies were flagged when self-reported depression or anxiety scores increased by >=5 points (a threshold considered clinically significant). The model achieved an adjusted F1-score of 0.80 (precision = 0.73, recall = 0.88) in detecting 393 symptom-worsening episodes across 341 participants, with higher performance observed for episodes involving concurrent depression and anxiety escalation (F1 = 0.84) and for more pronounced symptom changes (>=10-point increases, F1 = 0.85). Model interpretability was supported by SHAP-based analysis, which identified resting heart rate as the most influential feature in 71.4 percentage of detected anomalies, followed by physical activity and sleep. Together, our findings highlight the potential of explainable anomaly detection to enable personalized, scalable, and proactive mental health monitoring in real-world settings.