Camera-based monitoring may improve response times, but accuracy, privacy and governance remain central to responsible adoption.
Falls are one of the most persistent safety risks in later life, and the technology market around them is changing quickly. Wearable pendants and manual call systems remain familiar, but newer platforms use computer vision to identify a fall, notify a designated responder and provide context about what happened. Kami Vision is one of the companies developing this approach through its KamiCare platform.
The need is substantial. The US Centers for Disease Control and Prevention says more than one in four adults aged 65 and older reports falling each year. Falls are associated with millions of emergency-department visits and more than a million hospitalisations annually in the United States. For care providers and families, the practical question is not whether monitoring matters, but how it can be introduced without overstating what the technology can do.
Fall detection moves beyond wearables
Wearable devices can be useful, but their effectiveness depends on people remembering to wear, charge and activate them. Camera-based systems take a different route: they analyse movement within a defined field of view and look for patterns associated with a fall. When the software identifies a possible incident, an alert can be sent to staff, a monitoring centre or a nominated contact for verification and response.
That distinction matters. A detection system is not an emergency service, a clinical diagnosis or a substitute for trained carers. Its value lies in shortening the time between an event and an informed response—provided the camera covers the relevant space, connectivity is available and the alert reaches someone able to act. False positives and missed events remain possible in any automated monitoring system.
Kami Vision’s path from care settings to the home
Kami Vision’s recent consumer messaging presents residential use as an extension of experience gained in senior-care environments. Mike Link, chief operating officer of KamiCare, has described the company’s work in terms of applying lessons from professional care settings to less structured home environments. The underlying challenge is real: a care facility can define camera placement, staffing responsibilities and escalation protocols, while a private home may have changing lighting, pets, visitors, furniture and more variable connectivity.
The chronology, however, requires precision. Kami Vision introduced an institutional fall-detection beta and commercial service in 2022, and it announced an at-home version later that year. The company’s newer residential push is therefore better understood as an expansion and refinement of an existing consumer proposition—not its first move into the home. This framing preserves the significance of the company’s care-sector experience without creating an inaccurate launch narrative.
Understanding the accuracy claim
Kami Vision publishes a fall-detection accuracy figure of 99.5%. On its product methodology page, the company says this figure is based on its internal review of more than 3,700 reported incidents in 2023. The calculation excludes incidents whose origins could not be determined and cases attributed to user error. Those details are essential because a headline percentage alone does not show how a system will perform in every residence, care setting or population.
The figure should therefore be described as company-reported internal performance rather than independent clinical validation. Buyers should ask how a vendor defines a fall, measures missed events, handles duplicate alerts and evaluates performance across different room layouts, mobility patterns, skin tones, clothing, lighting and assistive devices. They should also understand whether performance data comes from live deployments, controlled tests or a mixture of both.
For technology providers, transparent reporting is more persuasive than a single accuracy number. Useful disclosure would include sensitivity, specificity, false-alert rates, incident-review procedures and known operating limitations. Independent evaluation would further strengthen confidence, especially when the product is used around people whose health or independence could be affected by a delayed response.
From alerts to prevention
The longer-term opportunity extends beyond detecting a person on the floor. Repeated changes in movement, room usage or transfer patterns may help care teams notice that someone needs a review. But such signals should be treated as prompts for human assessment, not automated medical conclusions. Mobility can change for many reasons, and interpreting those changes requires clinical and personal context.
This is where fall-detection products can fit into a wider prevention programme. The CDC’s STEADI framework encourages healthcare professionals to screen for fall risk, assess modifiable factors and intervene through measures such as medication review, strength and balance support, vision care and home-safety improvements. Monitoring technology may contribute information, but it cannot replace that multifactorial work.
Privacy is part of the product
A camera in a care facility or private residence creates an unavoidable privacy trade-off. Blurring, edge processing and restricted live access can reduce exposure, but privacy depends on the complete operating model: what is recorded, where it is processed, how long it is retained, who can access it, whether it is used to train models and what happens after a security incident.
Kami Vision’s privacy policy states that its services may process recorded video, use service providers and, with customer consent, use end-user video in the development or training of artificial-intelligence systems. Prospective users and care organisations should review the current terms that apply to their product and jurisdiction, rather than relying on broad statements such as “privacy first.” Consent must also be meaningful for residents, visitors and care workers who may enter the monitored space.
Organisations evaluating any vision-AI system can use the NIST AI Risk Management Framework and NIST Privacy Framework to structure questions about governance, testing, transparency and privacy risk. US healthcare entities and their business associates must additionally assess whether the deployment involves protected health information and how applicable HIPAA Security Rule obligations are met. Requirements will differ by country, contract and care model.
What buyers should understand
Before a system goes in, families and operators should have a clear picture of how it will actually work in the home:
● Which rooms are covered, and where blind spots remain;
● Who receives alerts;
● How quickly someone is able to respond; and
● How everyone living in or visiting the home is informed that the device is there.
Cost also matters. Senior care is expensive: Genworth and CareScout's 2024 survey placed the US median annual cost at $70,800 for assisted living and $127,750 for a private nursing-home room. Technology that supports safer ageing in place may be attractive to families, but affordability does not by itself demonstrate effectiveness.
The consumer test
Kami Vision’s experience illustrates both the promise and the burden of moving vision AI from managed care environments into private homes. Institutional deployment can generate operational lessons about camera placement, alert workflows and incident review. Consumer adoption introduces a different standard: the product must be simple enough for families to install and understand, while its privacy controls and limitations remain clear to people who may never read a technical specification.
Success will depend less on whether the technology can produce an alert in ideal conditions and more on whether it performs reliably within a complete care process. That means accurate claims, accountable human follow-up, transparent data practices and evidence that reflects real homes as well as professional facilities. Kami Vision can contribute meaningfully to that market, but—like every company operating in safety-sensitive AI—it should be judged by the quality of its evidence and governance, not only by the sophistication of its algorithms.
Sources and further reading
2. CDC — STEADI: Older Adult Fall Prevention
3. Kami Vision — Fall Detection Methodology
4. Kami Vision — Privacy Policy
5. Business Wire — Kami Vision Announces At-Home Fall Detection (2022)