CareNet: Linking Home-router Network Traffic to DSM-5 Depressive Behavior Indicators

2025-11-16

AI Summary

This study addresses non-intrusive mental health monitoring by proposing a depression-risk assessment method leveraging metadata from household routers—without payload inspection. Motivated by DSM-5 diagnostic criteria for depression, it extracts temporal behavioral features (e.g., HTTP request latency, session rhythmicity) from HTTP headers to construct clinically aligned behavioral indicators. The authors introduce FASL, a novel model that: (i) employs fuzzy membership functions to capture the continuous nature of depressive symptoms; (ii) adopts an additive aggregation mechanism for multi-dimensional behavioral integration; and (iii) incorporates temporal gating to enable longitudinal, dynamic assessment. Critically, the approach eliminates content-based privacy risks. Evaluated on real-world residential network traffic, FASL effectively identifies depression-associated patterns—including delayed sleep onset and attentional instability—with strong interpretability and practical deployability.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Digital mental-health sensing increasingly depends on mobile or wearable devices that require intrusive permissions and continuous user compliance. We present CareNet, a router-centric system that transforms household network metadata into interpretable behavioral indicators aligned with DSM-5 depressive-symptom domains. All processing occurs locally at the home gateway, preserving privacy while maintaining visibility of temporal routines. The core contribution is the Fuzzy Additive Symptom Likelihood (FASL), a transparent formulation that fuses header-level metrics into daily criterion-level likelihoods using bounded fuzzy memberships and additive aggregation. Combined with a DSM-style temporal gate, FASL integrates short-term traffic fluctuations into persistent, clinically interpretable indicators. Evaluation on realistic multi-day traces shows that CareNet captures characteristic patterns such as delayed sleep timing and attentional instability without payload inspection. The results highlight the feasibility of reproducible, explainable behavioral inference from router-side telemetry.

Problem

• Detecting depressive behaviors from home router traffic patterns
• Transforming network metadata into DSM-5 clinical indicators locally
• Monitoring mental health without intrusive device permissions

Innovation

• Router-centric system transforms network metadata into behavioral indicators
• Fuzzy Additive Symptom Likelihood fuses metrics into clinical likelihoods
• Local processing preserves privacy while capturing temporal behavioral patterns