Emanate’s RadioVision™ Wins Edge AI Blueprint Award, Solving Healthcare’s Room-Level Accuracy Challenge with 99.9% Precision

There’s a reason most hospitals still don’t have reliable room-level location intelligence. It’s not a lack of need.

It’s that existing systems haven’t been able to deliver clinical-grade accuracy without costly, proprietary infrastructure.

For years, healthcare providers have been forced into a tradeoff. They could deploy expensive systems built on wired infrastructure and vendor-locked technologies that deliver accuracy, or adopt lower-cost solutions that fall short of what real clinical workflows require. In environments where patient care, staff safety, and compliance depend on precise room-level awareness, that tradeoff simply doesn’t work.

RadioVision™ Combines Clinical-Grade and Room-Level Accuracy

And it’s why Emanate Wireless, in collaboration with Silicon Labs, has been recognized with the Edge AI Foundation 2026 Blueprint Award. This award recognizes a fundamental shift from proprietary, infrastructure-heavy RTLS systems to a standards-based, edge-driven architecture capable of delivering 99.9% indoor room accuracy in real-world healthcare environments.

A Different Way to Solve the RTLS Healthcare Problem

Instead of trying to continuously estimate where something is inside a room, RadioVision™ focuses on something far more reliable: knowing exactly when something enters or exits.

That shift changes the architecture entirely.

With RadioVision™, the doorway becomes the point of intelligence. A battery-powered BLE 5.1 Angle-of-Arrival chokepoint sits at the threshold and uses on-device machine learning to classify movement in real time.

Not where something might be.

But whether it is in the room or not.

That distinction is what enables 99.9% indoor room accuracy in real hospital environments, without wired infrastructure or proprietary reader-to-tag ecosystems.

Leveraging Edge AI Results in Accurate and Practical Deployments

Indoor wireless signals are inherently complex. Reflections, interference, and constant motion make it extremely difficult for traditional algorithms to determine precise location reliably.

RadioVision™ takes a fundamentally different approach.

Instead of trying to force accuracy through more infrastructure or signal processing, it uses machine learning trained on real-world movement patterns to classify entry and exit events directly at the doorway.

All processing happens on-device, at the point where the event occurs. High-bandwidth AoA data is converted locally into deterministic room-state events, and only those events are transmitted upstream.

As Neil Diener, CEO and co-founder of Emanate Wireless, explains:

“The industry has been trying to solve room-level accuracy by adding more infrastructure. We took a different approach. By focusing on the doorway and using edge-based machine learning, we’re able to deliver 99.9% accuracy without the cost and complexity that have limited adoption for years. Silicon Labs’ low-power wireless and on-device AI capabilities were a key part of making that architecture practical and scalable.”

This is what makes the system both highly accurate and practical to deploy at scale.

Room-Level Accuracy Means Real Impact in Healthcare

When room-level accuracy becomes reliable, it stops being a feature and becomes foundational infrastructure.

• Hospitals can automatically cancel nurse calls when staff enter a room.
• Staff duress alerts become precise and actionable.
• Hand hygiene compliance can be monitored with confidence.
• Assets can be tracked based on actual usage, not estimates.
• Patient–staff interactions can be understood in real context.

These are not incremental improvements. They directly impact patient outcomes, staff efficiency, and operational visibility.

Emanate’s Clinical-Grade RTLS is Powered by Silicon Labs

RadioVision™ is built on Silicon Labs’ EFR32MG26 Multiprotocol wireless SoC and EFR32BG22 Bluetooth SoCs, combining low- power wireless technology, Angle-of-Arrival capability, and embedded AI/ML acceleration.

The BG22 acts as the Angle-of-Arrival receiver, collecting CTE packets and phase information through the antenna array, while the MG26 processes that data and runs machine learning inference on Silicon Labs’ integrated AI accelerator. This combination allows RadioVision™ to classify doorway crossings with 99.9% accuracy, even in the multipath- and interference-rich environments typical of hospitals.

Silicon Labs’ low-power wireless architecture also helps eliminate the need for dense, wired infrastructure. Instead of requiring power-over-Ethernet installations and specialized reader networks, RadioVision™ can be deployed as a compact, battery-powered chokepoint with up to five years of battery life under typical hospital workloads.

The result is a scalable, energy-efficient RTLS model that lowers infrastructure cost from more than $1,000 per room to just over $100, making clinical-grade room accuracy far more accessible for hospitals of all sizes.

A New Blueprint for Edge AI

The Edge AI Foundation Blueprint Award recognizes more than performance. It highlights architectures that fundamentally rethink how problems are solved.

RadioVision™ represents that shift.

It replaces proprietary, infrastructure-heavy systems with a standards-based, battery-powered approach built on deterministic boundary intelligence and on-device machine learning.

While developed for healthcare, this architecture extends far beyond it. Any environment that requires reliable, real-time indoor awareness can benefit from this model.

Receiving the Edge AI Foundation Blueprint Award is Just the Beginning

We are honored to receive the Edge AI Foundation 2026 Blueprint Award and proud to collaborate with Silicon Labs in bringing this innovation to market.

RadioVision™ proves that achieving 99.9% indoor room accuracy does not require complex infrastructure or proprietary ecosystems. It requires a different way of thinking.

And this is just the beginning.