The third post in our series exploring Openwater’s vision, technology, and community.
Stroke kills more people worldwide than any cause except heart disease, and ranks fifth in the United States.1 Among strokes, large vessel occlusions (LVO) blockages in the major arteries feeding the brain do the most damage. LVOs make up roughly a third of ischemic strokes but account for a disproportionate share of deaths and lasting disability. Every minute an LVO goes untreated, nearly two million neurons die.2
A treatment exists. Mechanical thrombectomy, where a catheter physically pulls the clot out, has been one of the biggest advances in emergency medicine in the past decade. The catch: thrombectomy is only available at comprehensive stroke centers, and the clock starts the moment blood flow is impeded. So the bottleneck isn’t treatment – it’s quickly figuring out who needs it and getting these patients to the right treatment centers.
Right now, confirming an LVO means getting a CT angiogram at a hospital with the right scanner and staff. Prehospital scales like RACE and LAMS aim to screen for LVOs in the field, but they rely on clinical observation rather than direct measurement of cerebral blood flow. Too many Large Vessel Occlusion (LVO) strokes are missed, and, conversely, too many patients who do not have an LVO are sent to specialized comprehensive stroke centers unnecessarily. When the window between a good outcome and permanent disability is measured in minutes, that gap between suspecting a stroke and confirming one matters.
That’s one of the problems Open-MOTION was built to solve.
Open-MOTION uses a technique called pulsed speckle contrast optical spectroscopy, or PSCOS.3 When coherent laser light enters tissue, it scatters off moving red blood cells and other structures, producing a pattern of bright and dark spots – a speckle pattern. The contrast of that pattern is directly tied to how fast blood is flowing: faster flow means lower speckle contrast, slower flow means higher contrast. By firing short pulses of near-infrared laser light at the head and capturing the resulting speckle patterns, Open-MOTION measures changes in cerebral blood flow with high temporal resolution.
What sets it apart from traditional diffuse correlation spectroscopy (DCS), the established optical method for measuring blood flow, is scale. Conventional DCS uses single-mode fibers that sample one speckle at a time. Open-MOTION uses a custom camera module with a 5-megapixel CMOS sensor optimized for near-infrared light, sampling millions of speckles simultaneously in each measurement. That ensemble approach dramatically improves the signal-to-noise ratio, which makes it practical to detect blood flow asymmetries that indicate an LVO.
The device operates at 785 nanometers, a wavelength chosen because it sits near the isosbestic point for hemoglobin – the point where oxyhemoglobin and deoxyhemoglobin absorb light equally. That means the measurement reflects total blood flow rather than being confounded by oxygenation state. From the raw speckle data, the system derives two key metrics: relative blood flow from changes in speckle contrast, and relative blood volume from changes in average speckle intensity.
Open-MOTION was designed to go where the patient is. The system consists of a lightweight, wearable arm or headset connected to a compact console.
The set contains the optical probe: a near-infrared laser source paired with the custom 5-megapixel camera module, housed in a form factor that can be placed against the arm or the forehead. The probe is fixed to the device using a custom holder designed for consistent positioning across patients. The modular design means the same optical platform can be adapted for placement on different parts of the body, extending its potential applications beyond the brain.
The console handles data acquisition, processing, and power. At its core is an STM32H7-series microcontroller running custom firmware that manages the laser timing, camera synchronization, and data pipeline. The console connects to a wall power source, though the overall footprint is small enough to sit on a bedside table or ride in an ambulance.
Developed with San Francisco’s Level Design Studio, the industrial design received a 2025 Good Design Award in the Medical category. Our target price is expected to be significantly lower than that of traditional medical devices performing similar operations, compared with the million-dollar-plus MRI systems and CT scanners currently used for stroke imaging. That cost trajectory is possible because of consumer-grade optics, open-source software, and the platform approach to hardware.
A scan with Open-MOTION takes approximately 70 seconds.
The headset is placed on the patient. The near-infrared laser fires short pulses of coherent light into the region of interest. The camera captures the resulting speckle patterns at high frame rates. The system generates cerebral blood flow waveforms – continuous traces of how blood moves through the vasculature, based on relative changes in speckle contrast over time.
These waveforms are then analyzed by a deep learning model trained to distinguish LVO patterns from non-LVO patterns. The model learns discriminative representations directly from raw speckle-contrast data, identifying signatures of impaired blood flow that indicate a major arterial blockage.
A bedside optical scan provides a result in just over a minute, from placement to completion. The technology uses no contrast dye, no ionizing radiation, and no radiologist at the point of care.
The key study on Open-MOTION’s stroke-detection capability was published in the Journal of NeuroInterventional Surgery.3 Patients presenting with suspected acute stroke at comprehensive stroke centers underwent bedside optical blood flow evaluation, with CT angiography as the ground truth. The patient population included confirmed LVO cases, ischemic non-LVO strokes, hemorrhagic strokes, and stroke mimics – a realistic mix that reflects what clinicians actually encounter in the emergency department.
Open-MOTION achieved 79% sensitivity and 84% specificity for detecting anterior circulation LVO. For context, the study compared Open-MOTION’s performance against the two most widely used prehospital stroke scales. The RACE scale achieved 60% sensitivity and 81% specificity. LAMS achieved 50% sensitivity and 81% specificity. Open-MOTION detected substantially more LVOs than either clinical scale, with comparable or better specificity.
The clinical significance of that sensitivity gap is real. A prehospital scale that misses 40-50% of LVOs means patients who need thrombectomy are being routed to hospitals that can’t perform it, losing time that their brains cannot afford. A tool that captures nearly 80% of LVOs from a 70-second bedside scan without requiring imaging equipment, contrast agents, or a radiologist fundamentally changes the triage calculation.
The system’s operating point can also be tuned. Emergency medical services teams could optimize for sensitivity when the priority is not to miss any LVOs, or for specificity when the priority is to reduce unnecessary transfers. That adaptability reflects the platform’s open, configurable nature.
A separate validation study, published in Neurophotonics, confirmed a strong correlation between Open-MOTION’s optical blood flow measurements and transcranial Doppler, the established gold standard for non-invasive cerebral blood flow monitoring during a controlled breath-hold maneuver. The waveform morphology matched closely, confirming that the device is measuring pulsatile waveforms.4
While LVO triage is the first clinical application, Open-MOTION is really a portable blood-flow platform – and blood flow matters in many clinical contexts. The University of Birmingham is currently using Open-MOTION in a rehabilitation study to explore whether electrical stimulation of the common peroneal nerve can improve cerebral blood flow more effectively than pneumatic compression during the critical first 48 hours after an ischemic stroke. Open-MOTION provides non-invasive blood flow monitoring, enabling this comparison at the bedside.
The platform’s robust design means optical probes can be placed on various parts of the body, not just the head. That makes it potentially useful for any condition where monitoring blood flow is critical, including cardiac monitoring, assessment of vascular disease, guidance during surgery, and rehabilitation. And because the software is open source, researchers don’t need to wait for Openwater to build an application for their specific needs. They can build it themselves.
Note: Open-MOTION is a research platform. It has not been cleared or approved by the FDA for clinical use. All references to clinical applications describe research investigations, not approved medical interventions.
The software that processes the optical data, runs the blood flow analysis, and classifies stroke patterns is available on GitHub under an open-source license. As with Open-LIFU, this is a design decision with consequences that go beyond principle.
Reproducibility matters here. When a research team publishes results using Open-MOTION, other teams can examine the exact processing pipeline that produced those results. They can run the same algorithms on their own patient data. They can evaluate the deep learning model’s decision boundaries. In a field where diagnostic accuracy claims are life-or-death, that kind of methodological transparency is not optional.
Open source also means adaptability. The 70-second LVO scan is one application built on the platform, but a portable device that measures cerebral blood flow in real time has applications well beyond stroke triage – from monitoring patients in the ICU to tracking recovery in rehabilitation settings. Researchers can adapt the analysis pipeline to their specific research question without waiting for a vendor to build a feature.
And then there’s validation at scale. For a diagnostic tool to reach clinical adoption, it needs to be validated across diverse patient populations, clinical settings, and use cases. An open platform enables parallel validation efforts at institutions worldwide, each contributing to the evidence base. The University of Birmingham’s rehabilitation study is an early example: a research team taking the platform in a direction Openwater didn’t originally design it for, thereby generating new clinical evidence.
If you’re a researcher studying cerebral hemodynamics, stroke outcomes, neurovascular monitoring, or rehabilitation, Open-MOTION hardware is available through Openwater’s research partnership program. The platform is configurable for your protocol, and the University of Birmingham partnership demonstrates the model: you bring the clinical question, we provide the community support.
If you’re a developer, the Open-MOTION codebase spans several repositories on GitHub. The openmotion-sdk is a Python library for communicating with the hardware. The openmotion-bloodflow-app contains the blood flow analysis application. And the openmotion-console-v2 repository has the firmware for the console’s STM32H7 microcontroller. Whether you’re interested in signal processing, deep learning for medical diagnostics, embedded systems, or Python development, there are meaningful ways to contribute.
If you’re a clinician working in emergency medicine, stroke care, neurology, or rehabilitation, your clinical perspective shapes how this platform evolves. What would make a bedside blood flow monitor useful in your workflow? What diagnostic questions could it help answer? The researchers using Open-MOTION today are defining the platform’s clinical direction; join us in that conversation on Discord or GitHub; the conversation is open.
Explore the code: github.com/OpenwaterHealth
Read the research: Favilla et al., “Portable cerebral blood flow monitor to detect large vessel occlusion in patients with suspected stroke,” Journal of NeuroInterventional Surgery, 2025.
Validation study: Favilla et al., “Validation of the Openwater wearable optical system,” Neurophotonics, 2024.
Join the community: openwaterhealth.github.io/openwater-community
Get started: openwaterhealth.github.io/openwater-community/ get-started.html
Questions? community@openwater.health
This is the third post in our series exploring Openwater’s vision, technology, and community. Previously: Inside Open-LIFU: How We Open-Sourced a Brain Treatment Platform. Next up: The Community, how open-source collaboration is accelerating medical device innovation.