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Updated: Aug 5, 2026
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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Routine-Deviation Detection in Smart-Home Sensor Networks Using GRU Prediction
Abeer Aman1, Rashmi Kumari1, Raja Omman Zafar1
1School of Information and Engineering, Dalarna University, 791 88 Falun, Sweden.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a smart-home sensor framework using Gated Recurrent Unit (GRU) networks to detect deviations from normal routines, enhancing independent living for older adults with interpretable alerts.
Area of Science:
- Artificial Intelligence
- Ubiquitous Computing
- Gerontology
Background:
- Smart-home sensor networks offer unobtrusive daily activity monitoring for older adults' independent living.
- Existing anomaly detection methods often lack interpretability, providing only scores or binary alerts without explaining routine deviations.
Purpose of the Study:
- To propose a two-stage framework for interpretable routine-deviation assessment using smart-home sensors.
- To enable personalized and visually understandable detection of deviations from normal daily routines.
Main Methods:
- A Gated Recurrent Unit (GRU) model predicts daily activity based on sensor data, identifying potential deviation days.
- Sensor data is aggregated, mapped to activity zones, and converted into daily routine profiles.
- A second stage uses radar profiles for visual assessment of deviations by human experts.
Main Results:
- The GRU framework achieved low RMSE (0.136-0.180) and MAE (0.126-0.138), outperforming baseline models.
- Participant-specific routine modeling proved crucial, as deviation-sensitive zones varied across individuals.
- The approach successfully combined automated detection with visual analytics for interpretable decision support.
Conclusions:
- The proposed framework provides a personalized and interpretable method for routine-deviation detection in smart homes.
- This technology supports ambient assisted living research by offering a proof-of-concept decision support workflow.
- Participant-specific modeling is essential for accurate and meaningful routine deviation analysis.
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