Simulation-first training
Generate labeled risk scenarios to train models before large hardware rollouts.
Research
Risk prediction and simulation-first safety intelligence.
The research framework blends simulated sensor streams with real-world data to forecast electrical, water, fire, gas, and environmental risks. This page summarizes the methodology and provides the IEEE draft paper.
Contributions
Core research contributions
These are the technical foundations behind the Your Guardian platform.
Multi-domain sensor fusion
Combine electrical, water, gas, fire, and air quality signals into a unified risk score.
Predictive timeline output
Forecast incident probability windows to guide preventative action.
Methodology
How the system learns
A compact view of the modeling pipeline that powers the demo and future deployments.
Data generation
Simulated sensor streams are produced for normal and hazardous states with controlled noise.
- Baseline household profiles
- Injected anomaly patterns
- Automatic label creation
Modeling approach
Sequence models and anomaly detectors estimate risk trajectories over time.
- Temporal feature windows
- Risk score calibration
- Interpretability layer
Evaluation
Validation plan
Real-world pilots will be used to validate precision, recall, and alert timing.
Pilot households
Install sensor kits and compare predictions against manual inspections.
Model calibration
Adjust thresholds based on observed false positives and negatives.
Partner dashboards
Share results with insurers and smart home partners for feedback.