Routine-Deviation Detection in Smart-Home Sensor Networks Using GRU Prediction.

Smart-home sensor networks enable unobtrusive monitoring of daily activity and are increasingly used to support independent living among older adults. However, many anomaly detection methods produce scalar anomaly scores or binary alerts without explaining how the detected behavior differs from a resident's normal routine. This paper proposes a two-stage framework for interpretable routine-deviation assessment using smart-home motion and door-contact sensors. In Stage 1, raw sensor streams are aligned on a two-second master calendar, aggregated into hourly event counts, mapped into functional household activity zones, and converted into daily routine profiles. A Gated Recurrent Unit (GRU) routine prediction model is trained using a three-day lookback window to predict expected daily zone-level activity. Candidate routine-deviation days are automatically identified from daily prediction errors. In Stage 2, the recent monitoring period is plotted as 24-h radar profiles against the learned routine model, allowing a human expert to visually assess deviations in timing, location, and severity. The workflow was evaluated using 28 days of smart-home data collected from multiple independent residents. The proposed GRU framework achieved RMSE values ranging from 0.136 to 0.180 and MAE values ranging from 0.126 to 0.138 across the four participants, consistently outperforming the Previous-Day Baseline and generally providing lower prediction errors than the Seasonal Naïve Baseline. These findings demonstrate the effectiveness of participant-specific routine modeling for personalized routine-deviation detection in smart-home environments. The results indicate that deviation-sensitive target zones differed across the four participants, suggesting the importance of participant-specific routine modeling. The proposed approach successfully links automated candidate routine-deviation identification with radar-based visual analytics, providing a proof-of-concept, personalized, and interpretable decision support workflow for ambient assisted living research.