Custom signal processing algorithms and purpose-built RF hardware. Millions of monitoring hours. Clinical-grade accuracy from electromagnetic signals.
Custom RF front-end. Purpose-designed antenna arrays. Signal processing optimized for human micro-movements in cluttered residential environments.
Millimeter-wave operation where electromagnetic propagation demands deep RF engineering expertise. We spent years understanding how these signals behave in real homes.
The hard problems: distinguishing fall from sit when radar returns are nearly identical. Detecting respiration at 2+ meters through walls. Rejecting interference from ceiling fans, pets, HVAC systems. Each required custom solutions.
Multi-stage filtering. Temporal-spatial correlation. Pattern recognition trained on millions of labeled events from real deployments.
Classification beyond detection: fall vs sit. Walk vs shuffle. Normal sleep vs respiratory distress. Each model required extensive clinical validation across thousands of real-world environments.
Years of development went into the signal processing pipeline—understanding which features matter, which can be ignored, and how to extract meaningful patterns from noisy radar data.
ML architecture designed specifically for temporal radar data. Models that understand movement patterns over time—not computer vision adapted for radar.
Unsupervised baseline learning combined with anomaly detection. Individual behavioral models (wake time ±18min, bathroom visits, activity patterns) that improve the longer the system is deployed.
Thousands of installations continuously refine the models. Each deployment contributes edge cases and environmental variations we never would have seen in a lab.
Radar fundamentally cannot reconstruct images. Physics-level constraint, not policy. Visual surveillance is physically impossible with this technology.
Electromagnetic signatures contain only range, velocity, micro-movement data. Clinical-grade monitoring without generating personally identifiable information. No video storage. No facial recognition.
The architecture prevents the privacy concerns inherent in visual monitoring. A fundamental advantage of working with electromagnetic signals instead of light.
Millions of monitoring hours. Hundreds of facilities. Thousands of sensors deployed across diverse real-world environments.
Every deployment contributes edge cases, environmental variables, interference patterns. The algorithms adapt through continuous field operation—learning that cannot be replicated in lab settings.
The more we deploy, the better the system gets. Each environment teaches us something new about how radar behaves in the real world.
Clinical-grade accuracy validated across thousands of real-world deployments.
"Achieving clinical-grade accuracy in uncontrolled residential environments took years. Deep expertise across RF engineering, signal processing, and machine learning. Thousands of deployments teaching us what works and what doesn't."
Custom hardware. Signal processing algorithms developed over years. Machine learning models trained on millions of real-world events. Clinical validation across hundreds of facilities.
Building clinical-grade monitoring from radar required deep expertise across RF engineering, signal processing, machine learning, and clinical validation. We spent years on problems most people didn't think were solvable.
The result: a system that monitors with clinical accuracy while respecting privacy in ways cameras never could.
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