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

TL;DR

CareNet is a privacy-preserving router-based system that infers depressive behavior indicators from household network traffic, using a novel fuzzy logic approach to interpret behavioral patterns aligned with DSM-5 criteria.

Contribution

The paper introduces FASL, a transparent fuzzy logic method for linking network metadata to clinical depression indicators at the household level.

Findings

Successfully detects sleep and attention patterns without payload inspection

Preserves user privacy by processing data locally at the home gateway

Demonstrates feasibility of explainable behavioral inference from network traffic

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.