How does Microtrace Work?
Traditional drinking fountains operate blindly after installation. MicroTrace transforms the fountain into a self-verifying system by layering sensing → physical state estimation → AI intelligence → spatial insight around a UV-C disinfection core.
End to End Flow
A step-by-step overview of the process from when water enters the system to when it exits
Sensor Layer: Measuring System Behavior (Raw Observations)
MicroTrace integrates a compact, low-power sensor suite that continuously monitors the physical conditions that determine whether UV disinfection is actually effective.
Kalman Filter Layer: Accurate Physical State Estimation
A proven state-estimation algorithm used in aerospace, robotics, and autonomous systems
What the Kalman Filter Does
The Kalman filter:
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Fuses multiple noisy sensor inputs
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Incorporates physical flow and UV exposure models
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Estimates hidden system states that cannot be directly measured
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Estimated States (What Actually Matters)
Instead of trusting raw sensors, MicroTrace estimates:
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True flow velocity
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Actual residence time
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Delivered UV dose
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System health indicators (lamp aging, clogging trends)
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Why This Matters
Microbial inactivation depends on UV dose = intensity × exposure time.
The Kalman filter ensures this calculation reflects real system behavior, not momentary sensor noise.
It also:
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Corrects for sensor drift
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Smooths transient fluctuations
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Detects slow degradation trends long before failure
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This layer converts data into physics-grounded truth.
AI Layer: Predictive Intelligence
In addition to stabilized system estimates, MicroTrace applies a transformer-based AI model trained on time-series sensor data.
What the AI Model Analyzes
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UV dose trends
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Flow variability patterns
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Pressure and turbidity changes over time
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Temperature-adjusted effectiveness
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What the AI Predicts
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Contamination risk level: low / moderate / high
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Early UV lamp degradation
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Flow instability or abnormal usage
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Filter clogging or biofilm growth patterns
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Why AI Is Necessary
Not all failures are abrupt. Many are slow, nonlinear, and context-dependent.
The AI distinguishes:
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Normal daily fluctuations
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vs. meaningful deviations that indicate risk
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This allows preventive intervention, not reactive response.
Geospatial Heatmap: System-Level Insight
Each MicroTrace unit logs anonymized, location-tagged safety data.
How It Works
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Latitude and longitude are included as model inputs
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The AI learns spatial correlations across buildings, campuses, or cities
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Contamination likelihood is predicted across locations
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Output
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Geospatial heatmaps showing risk intensity
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Identification of systemic plumbing issues
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Prioritization of maintenance and upgrades
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This turns isolated fountains into a public-health monitoring network.