These vendors don't compete head-to-head. They own different layers of a stack. Apples-to-apples means scoring within a layer, then asking two questions: who is strongest in each layer, and which combination assembles best-in-class across all of them when no single vendor wins. Eight functional layers are scored, plus interoperability as a cross-cutting dimension on top, nine columns in all. Every player is scored on the same axes, vendor-neutral.
The layers, in one line: Sensing (detect an event) · Fall risk (predict who will fall) · Assessment (functional / mobility) · Cognitive (decline screening) · Analytics (fuse the signals) · Workflow (orchestrate care) · eCall (nurse-call / alerting) · Integration (connect the stack), plus Clinical interoperability (exchange with the medical record). RTM = Medicare reimbursement for remote monitoring; FHIR = the clinical-data exchange standard; CCRC = a continuing-care community spanning independent living through skilled nursing.
Where each player actually operates. Platforms light up many cells; specialists own one or two. The pattern is the point: a vendor that fills two cells and rents a third is a specialist wearing platform clothing, regardless of how it markets itself.
Who's in, and what the marks mean. The fifteen are the vendors active in AI-enabled senior-living care that recur in operator evaluations and public deployments: sensing, assessment, cognitive, and analytics specialists plus the platforms that span them. Full = owns the capability outright; partial = real but bounded (e.g., senior-living-EHR interfaces rather than full clinical exchange); limited = shallow or early; partner-enabled = delivered through a third party; not present = not offered; not disclosed = not publicly verifiable. This is a directional read of public positioning as of June 2026, not a weighted score.
| Vendor | Sensingdetect | Fall riskpredict | Assessfunctional | Cognitivedecline | Analyticsfusion | Workfloworchestr. | eCallnurse-call | Integ.data fabric | Clin. interopFHIR/EHR |
|---|---|---|---|---|---|---|---|---|---|
| Platforms: span multiple layers | |||||||||
| InspirenCV ecosystem · AUGi | |||||||||
| SafelyYoucomputer vision | |||||||||
| CarePredictwearable | |||||||||
| Sagemulti-modal · Sage Detect | |||||||||
| Sensing specialists | |||||||||
| Vayyarradar · camera-free | |||||||||
| care.aiambient CV | |||||||||
| Foresitepassive + predictive | |||||||||
| Caspar AIcontactless · w/ Icon | |||||||||
| Assessment specialists | |||||||||
| SeniorLife.AIgait video + gaze | |||||||||
| OneStepphone gait · RTM | |||||||||
| Cognitive specialists | |||||||||
| Linus Healthdigital clock + recall | |||||||||
| BrainCheck3-min screen | |||||||||
| Analytics & integration | |||||||||
| Fynn.iopredictive analytics | |||||||||
| SentricsEnrich · data fabric | |||||||||
| K4ConnectFusionOS · eCall | |||||||||
The within-layer winner is the apples-to-apples answer. Note the deliberate split between fall detection (a fall happened; sensing/eCall) and fall-risk prediction (who will fall; the assessment seam): different capabilities, different winners. And the gap that drives everything downstream still holds: no vendor owns cognitive and full platform at once.
Two different questions, two different answers. If you must name one vendor (the safe default, not the best outcome), Inspiren takes the platform crown on breadth, capital, and 100+ named communities, with SafelyYou the pick where memory-care falls are the whole job (deepest peer-reviewed evidence) and CarePredict where one system must span the full continuum. But the honest answer is that no single vendor wins every layer. Every platform is weak-to-absent on cognitive, and assessment depth lives with the specialists. The single-vendor crown is a compromise; the stack (Analysis 03) is the actual recommendation.
When no one wins outright, the right move is the minimum set of vendors that assembles best-in-class across every layer. Three configurations, each tuned to a different buyer priority. The recurring lesson: the assessment + cognitive layer is a slot best filled by a specialist, never by stretching a sensing platform to cover it.
A predictive model is capped by the data it can see. Fall risk, decline, and deterioration are multifactorial: polypharmacy, orthostatic hypotension, a recent UTI or hospitalization, a year of gait history. A model reasoning from a single stream (one vendor's radar, wearable, or camera feed) is structurally limited no matter how good its algorithm; it is blind to the rest of the picture. So the real lever isn't any one protocol. It's how much relevant longitudinal data the model can draw on.
That places every vendor on a spectrum of data richness. Narrow and point-in-time: single-modality sensing, little history retained. Broad but siloed: multi-modal platforms with deep longitudinal operational data (gait, sleep, activity, eCall history), but no clinical chart; this is genuine breadth, and where the strongest platforms live. Clinically integrated: able to read the longitudinal medical record: medications, comorbidities, labs, prior events. Each step widens what the model can reason from.
Two distinct moves are worth separating. Reading the record in is what improves prediction: more inputs, better estimates. Writing results back is a workflow benefit (it closes the loop into where staff work) but does nothing for accuracy. FHIR/EHR exchange is one path to clinical read-in; rich multi-modal sensing is another path to breadth. Don't conflate the goal (data richness) with any single means of reaching it.
Where this doesn't decide it: for a narrow, acute job (memory-care fall detection, where the task is catching the fall in the moment), a deep clinical record adds little, and a best-in-class single-modality detector wins outright. This argument is about prediction, not detection.
The honest bottom line: broader data should improve predictive accuracy, but the size of that effect is not yet independently quantified in this market. All else equal, weight vendors that can widen their data aperture: by clinical read-in, by multi-modal breadth, or both, and ask each to show what is live today versus on the roadmap. This is a market judgment, not an endorsement of any vendor.