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4-Layer Sensor Fusion Model
True occupancy intelligence requires fusing multiple sensor modalities. Click each layer to explore how data flows from raw signals to actionable building intelligence.
Layer 1 — HVAC Telemetry
Supply/return temps, valve positions, airflow rates, VFD speeds. The baseline mechanical signal.
What it measures: Zone temperatures, supply air temperature, return air temperature, chilled water valve position, fan VFD speed, damper positions, and thermal load calculations.
Accuracy alone: 40–55% for occupancy detection. Thermal lag creates 15–30 min blind spots. Cannot distinguish between solar load and human presence.
Key limitation: Reactive, not predictive. Tells you what happened, not what's happening now.
Layer 2 — CO2 / IAQ Sensors
CO2 concentration as a proxy for occupancy density. Adds the "breath signature" of a zone.
What it measures: CO2 parts per million (ppm), typically 400 ppm (outdoor baseline) to 1200+ ppm (high density). Also VOCs, PM2.5, humidity in advanced IAQ sensors.
Accuracy with Layer 1: 60–70%. CO2 buildup correlates to person-count but has 8–15 min latency due to air mixing. Windows and ventilation rates introduce noise.
Key insight: CO2 delta (rate of change) is more valuable than absolute level. Fast rise = recent arrival. Slow decay = zone clearing.
Layer 3 — Wi-Fi / BLE Positioning
Device association counts from APs. Uniquely identified device count = person count proxy.
What it measures: Connected and probing devices per access point. RSSI triangulation for zone-level positioning. Dwell time per zone. MAC randomization handled via association-based counting.
Accuracy with Layers 1+2: 80–88%. Real-time (sub-minute), unique device count, zone granularity. But: visitors without Wi-Fi, multi-device users, and guest vs. employee create noise.
Key advantage: Only modality that provides spatial flow data — movement patterns, zone transitions, peak corridors.
Layer 4 — Motion / PIR
Passive infrared and active motion detection. The ground-truth binary: someone is here, or they aren't.
What it measures: Binary presence/absence (PIR), motion direction (dual-element PIR), people counting (ToF/LIDAR sensors at doorways). Sub-second response time.
Fused accuracy (all 4 layers): 92–97%. PIR eliminates the "phantom occupancy" false positives that CO2 and Wi-Fi create. Doorway counters provide ground-truth calibration for all other layers.
The fusion payoff: HVAC alone = 45%. Add all 4 layers = 95%+. That 50-point accuracy jump is the difference between 15% and 28% energy savings.
Fusion Accuracy Model
| Layers Combined | Accuracy |
|---|---|
| HVAC Only | ~45% |
| + CO2 | ~65% |
| + Wi-Fi | ~85% |
| + Motion (Full Fusion) | ~95% |
PropTech Market Pulse
Real-time tracking of the trends shaping AI-powered building intelligence. Data refreshed weekly from industry sources.
4-Layer Sensor Fusion Architecture
Click each layer to explore how fusion eliminates false positives
HVAC Sensors
Temperature · Humidity · Airflow · Valve Position
45%
CO₂ Monitoring
Concentration · Ventilation Rate · Air Quality Index
+20% → 65%
Wi-Fi Analytics
Device Count · Dwell Time · Zone Density
+20% → 85%
Motion / PIR
Presence Detection · Movement Patterns · Zone Coverage
+10% → 95%
Cumulative Accuracy
Accuracy by Fusion Depth
Single Layer
45%
Dual Fusion
65%
Triple Fusion
85%
Quad Fusion
95%
CRE & PropTech Market Signals
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Practitioner Tools & Resources
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