Nura doesn't compare your parent to a population average. It learns their normal — across sleep, movement, heart rate, heart rate variability, skin temperature and blood oxygen — and tells you when something has changed enough to matter.
Nura's sensors were chosen by working through the published research on what a wrist can reliably read — and what it can't. Each is a high-precision, research-validated component. No single sensor tells you much on its own; the value is in reading several together, over time, against one person's own baseline. From these same sensors, Nura derives a wider set of signals without adding hardware.
The foundational sensor. Uses light to read blood flow at the wrist, following heart rate and heart rate variability across the day and night. A single reading tells you almost nothing. Weeks of readings tell you what's usual for this person — and make a small, sustained shift visible.
Reads blood oxygen saturation (SpO₂) at the wrist and learns each person's typical range. Nura reports changes relative to that personal range rather than against a fixed threshold.
Follows movement in every direction. Builds a picture of daily activity, sleep timing, and the shape of a normal day — how much someone moves, when, and at what pace. For older adults the useful signal is rarely a step count; it's whether this week looks like the weeks before it.
Recognises the distinctive motion signature of a fall — a sudden high-G impact, preceded by free fall or rapid rotation, followed by stillness. Machine learning confirms it's a real event rather than someone sitting down heavily. When Nura sees one, the family is alerted immediately, with location.
High-precision skin temperature, followed continuously. Nura learns each person's own temperature rhythm across days — including how it moves with their body clock — and notices sustained departures from it.
No single sensor gives you the whole picture, and any one of them alone produces noise. Nura's core technical work is in reading several signals together, over time, against one person's baseline — which is what the research consistently shows produces the fewest false alarms. Nura is built to say something only when several signals agree and the change persists.
Nura has no stress sensor. It reads stress-related patterns from heart rate and heart rate variability — the beat-to-beat changes research ties to the body's stress response — and follows them over time, which is what separates a hard afternoon from a hard month.
No wrist device can measure hydration directly, and Nura doesn't claim to. Nura reads hydration risk by looking at rising resting heart rate, falling heart rate variability and skin temperature patterns together. Thirst becomes less reliable with age, which is why a passive signal is useful here.
For each signal Nura follows, there's a body of peer-reviewed work establishing that it's measurable at the wrist and meaningful over time in older adults. That research is why we built Nura the way we did.
The research below establishes what wrist-worn sensors can measure in principle, in the populations those studies examined. It is not a measurement of Nura's performance. Our own validation work is underway, and we'll publish it when we have it.
The most consistent finding across wrist-wearable research is that measurable physiological change — resting heart rate, sleep, activity — appears before a person feels different. The body shifts before anyone would think to look. That's why Nura is built around each person's own baseline rather than a set of thresholds: a threshold only fires once something is already obviously wrong, and by then the family usually knows. A baseline can see a change that's unremarkable for everyone else but unusual for this person.
The most thoroughly studied application in wearable health technology. A 6-axis motion sensor captures the signature of a fall — a high-G impact spike, preceded by free fall or rapid rotation, followed by inactivity — and machine learning distinguishes a real fall from someone dropping into a chair. Reviews of the literature find that validated systems consistently use accelerometer and gyroscope signals with machine-learning classification, which is the approach Nura takes.
Heart rate and motion together support detailed sleep tracking — timing, quality, stages, regularity and nighttime movement. Nura's screenless, all-day design helps here for an unglamorous reason: people actually keep a bracelet on at night, where a device that needs nightly charging comes off.
No wrist sensor reads hydration directly. But changes in hydration produce measurable changes elsewhere in the body, and those are readable at the wrist: resting heart rate, heart rate variability, skin temperature and activity. Nura reads them together and notices when the combined picture shifts.
The body's stress response changes how the heart beats — faster, with less variation between beats. Nura's optical sensor reads those beat-to-beat intervals, which gives a non-invasive window into how someone's body is handling the day. Over weeks, it separates a bad afternoon from sustained strain.
Nura follows wrist skin temperature continuously and builds a personal baseline over the first 7–14 days of wear. Rather than checking against a fixed number, it notices when someone's own pattern shifts and stays shifted — including changes in the daily rhythm itself.
Wrist accelerometers give validated activity classification, but for older adults the useful information is in the pattern rather than the count. A week of unusually quiet days says more than any single day's steps. Nura follows activity level, routine consistency and movement trends.
Everything above is what Nura does today, as a wellness product. Below is what we're researching — and being direct about it: these are not features Nura offers. Each would require FDA clearance before we could put it in front of a family, and pursuing that clearance is a deliberate part of our roadmap rather than something we've done.
The reason this matters for the people wearing Nura today: our hardware is already capable of supporting this work. Cleared capability, when it arrives, arrives as a software update — not a new bracelet.
Fall detection reacts after a fall. Understanding fall risk means following walking patterns, activity, sleep and physical trends over time to see when someone is more vulnerable. There's a substantial research literature here. Turning it into something we tell a family requires clearance.
Research on multi-sensor wearables shows that changes across several signals — heart rate, heart rate variability, activity, sleep, temperature — often move together in the days before a significant health event. Reading that pattern reliably enough to act on it is an active research question, and one we're working on with our own population.
Most published wearable research uses younger, healthier participants. Older bodies behave differently, and sensors behave differently on them. Building and validating on data specifically from adults over 70 is our central research priority — and the reason our own validation work matters more to us than anyone else's published numbers.
The science behind Nura draws on peer-reviewed work across clinical medicine, medical engineering and aging research. These are among the findings that informed how we built it.
The research is consistent: several signals read together outperform any single signal, and produce far fewer false alarms. We built that in as a design principle rather than adding it later. It's also why Nura stays quiet unless a change shows up across more than one signal and persists.
"Normal" varies enormously between people, and published averages are mostly drawn from younger, healthier bodies. Nura learns each person's own patterns over the first 7–14 days before it says anything at all.
We say "hydration risk," not "dehydration." "Sleep signals," not "insomnia." "A change in their temperature pattern," not "a fever." This isn't legal caution dressed up as principle — it's a more accurate description of what a wrist can actually know, and we'd rather tell you what we see than tell you what it means.
Nura will tell you that something about your mother's week looks different from her usual. It will not tell you why, and it will not tell you what to do about it. That's a conversation for her and her doctor — and Nura's job is to make sure that conversation happens sooner than it otherwise would.
Sensors behave differently on older skin and older vessels. Building and validating on data from the people who actually wear Nura is our core R&D commitment, and it's the work we intend to publish.
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A selection of the peer-reviewed research that informs Nura's design. Full bibliography available on request.