{"id":35249,"date":"2026-05-15T08:00:00","date_gmt":"2026-05-15T06:00:00","guid":{"rendered":"https:\/\/keplervision.eu\/?post_type=seoai_post&#038;p=35249"},"modified":"2026-08-11T13:45:15","modified_gmt":"2026-08-11T11:45:15","slug":"which-technology-is-used-for-fall-prevention-for-elderly","status":"publish","type":"seoai_post","link":"https:\/\/keplervision.eu\/en\/blog\/which-technology-is-used-for-fall-prevention-for-elderly\/","title":{"rendered":"What technology is used in fall prevention for the elderly?"},"content":{"rendered":"<p><em>Updated 4 Aug 2026<\/em><\/p>\n<p>If you are reading this, you are probably comparing suppliers. The material available to you is mostly marketing. So let me write the article I would want to read if I were in your chair.<\/p>\n<p>In what follows:<\/p>\n<ul>\n<li>First, I explain that the night shift is the real problem, and not falls as such.<\/li>\n<li>Second, I go over the five types of fall technologies available.<\/li>\n<li>Third, why &#8220;detection versus prevention&#8221; is the wrong distinction, and what the right one is.<\/li>\n<li>Fourth, how to judge a technology supplier\u2019s accuracy claims, including ours.<\/li>\n<li>Fifth, how about privacy and fall prevention technology.<\/li>\n<li>Sixth, the two questions that are important to choose the technology for your organization.<\/li>\n<\/ul>\n<h2><strong>Start with the night, not with falls<\/strong><\/h2>\n<p>Falls are the second leading cause of unintentional injury death worldwide, and around 37 million a year are severe enough to require medical attention.<sup>[1]<\/sup><\/p>\n<p>In the United States, more than one in four people over 65 reports falling each year \u2014 over 14 million people \u2014 and fewer than half of them tell their doctor.<sup>[2][3]<\/sup><\/p>\n<p>All true, and every supplier\u2019s website carries numbers like these. They still tell you nothing you can act on.<\/p>\n<p>Here is the version that does. In a care home at three in the morning, one caregiver may be responsible for thirty residents. She cannot be in thirty rooms at once. So she walks rounds, which wakes people up, and between rounds she is blind. A resident who falls on the way to the bathroom lies on the floor until the caregiver happens to look. That waiting time is the thing you are actually buying down.<\/p>\n<p>Marlou Kuijper, a coordinating nurse at Joriszorg, put it better than I can. What she values most is that she no longer has to disturb her clients\u2019 sleep to check on them. And when an alarm does come in, she can ask a colleague to bring the patient lift (hoist) before she walks into the room, instead of finding out what happened first and then arranging help.<sup>[5]<\/sup><\/p>\n<p>That is what this fall detection technology is for.<\/p>\n<p>What types of technology are used for fall detection and prevention?<\/p>\n<p>Various types of technology exist for fall detection and prevention, each with their own advantages and applications. The main categories are wearable devices, environmental sensors, camera systems, and smart floor systems.<\/p>\n<p>Wearable devices such as smartwatches and fall detection pendants are popular due to their ease of use. These devices contain accelerometers and gyroscopes that can detect sudden movements indicating a fall. They can automatically send alerts to caregivers or family members when a fall is detected.<\/p>\n<p>Environmental sensors are strategically placed in living spaces and can monitor movement patterns. These sensors can detect unusual activity, such as prolonged inactivity or abrupt movements. Camera systems with artificial intelligence offer the most advanced solutions, using image recognition to identify fall risks and detect actual falls in real time.<\/p>\n<h2><strong>The five families of technology, and where each one fails<\/strong><\/h2>\n<ol>\n<li><strong> Wearables. <\/strong>Pendants, smartwatches, belt clips. An accelerometer and a gyroscope can detect an impact. They are cheap, and they work \u2014 when worn and when you fall down hard.<\/li>\n<\/ol>\n<p>There are two failure modes. The first is human: the device is on the charger, in a drawer, or on the bedside table, and residents with cognitive decline are the least likely to wear it and the most likely to fall. The second is physical: a resident who slides slowly out of an armchair, or buckles onto the edge of the bed, may never produce an impact large enough to cross the threshold.<\/p>\n<ol start=\"2\">\n<li><strong> Passive infrared and environmental sensors. <\/strong>Motion detectors in the room, sometimes combined with door contacts. They tell you that something moved, or that nothing has moved for a long time. But passive infrared sensors cannot articulate what goes on in the room. The door moved, but did someone enter or exit? Something close to the bed moved, but was it a resident getting out, or a pillow falling down?<\/li>\n<li><strong> Bed sensors. <\/strong>Pressure mats under the mattress, sometimes beside it. Good at one specific thing: knowing whether somebody is in bed. Blind everywhere else in the room.<\/li>\n<li><strong> Radar and WiFi sensing. <\/strong>Genuinely interesting, and I follow the research closely. Radio waves reflect off a body and the reflections change when the body falls. No camera, which people like.<\/li>\n<\/ol>\n<p>But the state of the art is not yet at the reliability a care organization needs. The systems struggle with more than one person in a room, and you cannot move the bed without recalibrating.<\/p>\n<p>The deeper problem is the data. Computer vision inherited the trillions of photographs that people have taken with their phones and published on Facebook or elsewhere on the internet, and every general-purpose vision model trained on that corpus lifts the whole field along with it. Radar has no equivalent. There is no vast public archive of radio reflections off falling bodies, and there is no way for one to come into existence. That gap does not close on its own; it widens every year that phone cameras keep pointing at the world. It is not the technology I would choose today to look after my own mother.<\/p>\n<ol start=\"5\">\n<li><strong> Cameras with artificial intelligence. <\/strong>This is what we build, so read the rest with that in mind. A smart sensor watches the room continuously and software decides what it is seeing. It works in the dark, needs nothing from the resident, and can recognize more than falls: out of bed, sitting on the edge of the bed, out of the room, lying in one position too long, restlessness in bed. It is the most capable option, and the one that requires a real conversation about privacy (later on in this blog).<\/li>\n<\/ol>\n<p>What is the difference between fall detection and fall prevention technology?<\/p>\n<p>Fall detection technology responds after a fall has occurred by automatically alerting help, while fall prevention technology attempts to prevent falls by identifying risk situations early and providing warnings before a fall occurs.<\/p>\n<p>Fall detection systems are designed to raise an alarm as quickly as possible after a fall. These systems focus on recognizing the characteristic movements and impact of a fall, so help can be alerted within seconds. This is crucial to minimize the time between a fall and medical assistance.<\/p>\n<p>Fall prevention technology, on the other hand, analyzes behavioral patterns and environmental factors to identify risk situations before a fall actually occurs. These systems can, for example, warn when someone gets up at night and walks to the bathroom, a moment when fall risk is increased. Through this proactive approach, caregivers can intervene before something happens.<\/p>\n<h2><strong>The distinction that matters is the long lie<\/strong><\/h2>\n<p>Suppliers, including us, have talked for years about &#8220;fall detection&#8221; versus &#8220;fall prevention.&#8221; I now think that framing is a bit of a marketing artifact. In a care home, three different things happen, and they need different answers.<\/p>\n<ol>\n<li><strong>A witnessed fall. <\/strong>Somebody is there. Technology adds little.<\/li>\n<li><strong>An unwitnessed fall. <\/strong>Nobody is there, and how long the resident lies on the floor determines much of the clinical outcome. This is where continuous monitoring earns its money, and where an event-triggered wearable in a drawer is worth nothing.<\/li>\n<li><strong>A situation heading towards a fall. <\/strong>A resident swings her legs over the edge of the bed at 02:40, and she is somebody who does not manage that walk unaided. Nobody has fallen. An alert now prevents the incident rather than reporting it.<\/li>\n<\/ol>\n<p>Ask technology suppliers which of those three they address. Most technologies only do the second (wearables, infrared, bedsensors). UZ Brussels uses our software mainly for the third: their nursing staff get alerted when confused or wandering patients get out of bed or leave their rooms.<sup>[5]<\/sup><\/p>\n<h2><strong>How to judge an accuracy claim, including ours<\/strong><\/h2>\n<p>The standard metric in computer vision is Average Precision. We compute ours over hundreds of thousands of test images, which is about two orders of magnitude more than a typical academic benchmark, so the number is reliable. It exceeds 99.99%.<sup>[4]<\/sup><\/p>\n<p>I am telling you that number so I can tell you it is useless to you. Once you are that close to 100%, Average Precision stops measuring progress, and it was never the thing a caregiver experiences. I have never once mentioned Average Precision to a nurse. If any supplier leads with a single accuracy percentage above 95%, ask them what it is a percentage of. Frequently there is no good answer.<\/p>\n<p>Two numbers do matter, and both are awkward to produce, which is precisely why you should ask for them.<\/p>\n<p><strong>Missed falls. <\/strong>A fall our software does not report. This is hard to measure because we can only know about it if a caregiver tells us, so we give care organizations a reporting tool and ask them to. Two years ago our customers reported about one missed fall per 277 video streams per year. Now it is about one per 416. Fitted over the twenty months to September 2025, that trend is a 44% reduction.<sup>[4]<\/sup><\/p>\n<p>Note that this is per stream, not per resident \u2014 a stream may cover a corridor or a communal room, so do not read 416 streams as 416 residents.<\/p>\n<p><strong>False alarms. <\/strong>Easier to count. Our own quality assurance on newly connected sensors showed the interval between false fall alarms going from 72 days to 148 days over the twelve months to March 2025. Caregiver-reported mistakes across all our alarm types fell by close to half over a comparable period.<sup>[4]<\/sup><\/p>\n<p>Some honesty about those numbers. Nurses are busy and do not report every mistake our software makes. The trend lines have low R-squared values (reliability). And lots of installations run on poor-quality sensors, which drags our own statistics down.<\/p>\n<p>Why does any of this improve? Thirty software updates in twenty months, thirty-nine retrained neural networks, three new architectures, and an ever-growing training set.<sup>[4]<\/sup><\/p>\n<h2><strong>Privacy is important<\/strong><\/h2>\n<p>Every care organization asks about cameras in bedrooms, and rightly. A promise not to look at the images is worth very little. What matters is where the processing happens and who, if anyone, watches the images.<\/p>\n<p>Our server product runs on-premise, inside your own network, on your own AI servers. Our embedded product runs on the camera itself, on the Mobotix C71, with the neural network on a chip a few centimeters from the lens. In normal operation, what reaches a caregiver\u2019s phone is an alarm \u2014 that a resident has fallen, or left the room \u2014 not a video feed. Nobody sits watching a wall of monitors, because there is no wall of monitors.<\/p>\n<p>There is one exception. When a care organization starts with us, we ask permission to review the alarms our software generates, so we can verify they were correct. And in the rare case where a caregiver reports a fall we missed, we ask to look at that footage so we can add it to our training data and stop missing that kind of fall. Both happen only with explicit permission, and we hold ISO 27001 and NEN 7510, allowing us to do just that.<\/p>\n<h2><strong>The two questions I would ask to make your choice<\/strong><\/h2>\n<p>Ninety-one care locations in the Netherlands run our software, plus sites in Belgium, Norway and beyond. I am not going to pretend that settles it.<\/p>\n<p>So here is what I would do in your position. Ask every supplier on your shortlist for two numbers: their missed falls per stream per year, and their mean number of days between false alarms. Then ask how each number was measured and by whom. Most cannot answer. That tells you something.<\/p>\n<p>And then test us. You can run our embedded software on a Mobotix C71 free for a month. If your system integrator is recommending something else, ask her to test us alongside it.<\/p>\n<p><a href=\"https:\/\/keplervision.eu\/en\/\">Contact us<\/a> to discover how our fall prevention technology can help your care organization improve patient safety and optimize staff deployment.<\/p>\n<h2><strong>References<\/strong><\/h2>\n<p>[1] World Health Organization. Falls (fact sheet), 26 April 2021. Accessed 3 August 2026. <a href=\"https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/falls\">https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/falls<\/a><\/p>\n<p>[2] Centers for Disease Control and Prevention. Older Adult Falls Data, updated 26 February 2026. Accessed 3 August 2026. <a href=\"https:\/\/www.cdc.gov\/falls\/data-research\/index.html\">https:\/\/www.cdc.gov\/falls\/data-research\/index.html<\/a><\/p>\n<p>[3] Centers for Disease Control and Prevention. Facts About Falls, updated 27 January 2026. Accessed 3 August 2026. <a href=\"https:\/\/www.cdc.gov\/falls\/data-research\/facts-stats\/index.html\">https:\/\/www.cdc.gov\/falls\/data-research\/facts-stats\/index.html<\/a><\/p>\n<p>[4] Stokman, H. Is It Worth Paying A Yearly Fee For Software Updates? Kepler Vision Technologies CEO blog, 14 October 2025. <a href=\"https:\/\/keplervision.eu\/en\/ceo-blog-is-it-worth-paying-a-yearly-fee-for-software-updates\/\">https:\/\/keplervision.eu\/en\/ceo-blog-is-it-worth-paying-a-yearly-fee-for-software-updates\/<\/a><\/p>\n<p>[5] Kepler Vision Technologies. Case studies. <a href=\"https:\/\/keplervision.eu\/en\/case-studies\/\">https:\/\/keplervision.eu\/en\/case-studies\/<\/a><\/p>\n\n<div class=\"wp-block-seoaic-faq-block\">\n    <h2 class=\"seoaic-faq-section-title\">Frequently Asked Questions<\/h2>\n            <div class=\"seoaic-faq-item\">\n            <h3 class=\"seoaic-question\">\n                How long does it take to implement a fall prevention system in a care facility?            <\/h3>\n            <p class=\"seoaic-answer\">\n                The implementation of a modern fall prevention system typically takes 1-3 days, depending on the size of the facility. Thanks to plug-and-play technology, most systems can be operational within hours, with minimal disruption to daily care activities.            <\/p>\n        <\/div>\n                <div class=\"seoaic-faq-item\">\n            <h3 class=\"seoaic-question\">\n                Can residents disable or bypass the fall prevention technology?            <\/h3>\n            <p class=\"seoaic-answer\">\n                Advanced camera systems cannot be disabled by residents, ensuring continuous protection. With wearable devices this is possible, which can be a disadvantage. Modern AI systems therefore often offer the most reliable 24\/7 monitoring without dependence on resident cooperation.            <\/p>\n        <\/div>\n                <div class=\"seoaic-faq-item\">\n            <h3 class=\"seoaic-question\">\n                What happens if the fall prevention system has a technical malfunction?            <\/h3>\n            <p class=\"seoaic-answer\">\n                Professional fall prevention systems have built-in redundancy and backup systems to prevent outages. In case of technical problems, administrators receive automatic notifications, and most suppliers offer 24\/7 technical support with fast response times for critical care applications.            <\/p>\n        <\/div>\n                <div class=\"seoaic-faq-item\">\n            <h3 class=\"seoaic-question\">\n                How are care staff trained in the use of fall prevention technology?            <\/h3>\n            <p class=\"seoaic-answer\">\n                Training usually consists of a short instruction session of 1-2 hours in which staff learn how to receive, interpret and respond to alarms. Most systems are intuitively designed with clear interfaces, so minimal training is required for effective use.            <\/p>\n        <\/div>\n                <div class=\"seoaic-faq-item\">\n            <h3 class=\"seoaic-question\">\n                Can fall prevention systems distinguish between different types of incidents?            <\/h3>\n            <p class=\"seoaic-answer\">\n                Yes, advanced AI systems can recognize different situations, such as falls, prolonged lying, unusual inactivity or emergency situations. They can also distinguish between normal activities and real emergencies, providing caregivers with specific information about the type of incident.            <\/p>\n        <\/div>\n                <div class=\"seoaic-faq-item\">\n            <h3 class=\"seoaic-question\">\n                What costs are associated with implementing fall prevention technology?            <\/h3>\n            <p class=\"seoaic-answer\">\n                Costs vary depending on the type of system and the size of the facility, but modern systems are often more cost-effective than traditional care methods. Many suppliers offer flexible subscription models, and the investment is often recouped through reduced staff costs and lower insurance payouts.            <\/p>\n        <\/div>\n                <div class=\"seoaic-faq-item\">\n            <h3 class=\"seoaic-question\">\n                How is the privacy of residents guaranteed when using camera systems?            <\/h3>\n            <p class=\"seoaic-answer\">\n                Modern AI systems process images locally and automatically, without people being able to view the images. Only relevant alarm information is forwarded to caregivers. All data is stored encrypted and systems comply with GDPR regulations and care sector-specific privacy standards such as NEN 7510.            <\/p>\n        <\/div>\n        <\/div>\n\n\n<!-- wp:themify-builder\/canvas \/-->","protected":false},"excerpt":{"rendered":"<p>AI fall prevention detects falls in elderly people with 95%+ accuracy. Discover modern technologies that are transforming healthcare organizations.<\/p>\n","protected":false},"author":1,"featured_media":33285,"template":"","categories":[],"tags":[],"class_list":["post-35249","seoai_post","type-seoai_post","status-publish","has-post-thumbnail","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What technology is used in fall prevention for the elderly? | Kepler Vision Technologies<\/title>\n<meta name=\"description\" content=\"Discover which technology is used in fall prevention for seniors: from AI systems to smart sensors. 95%+ accuracy, 24\/7 monitoring.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/keplervision.eu\/en\/blog\/which-technology-is-used-for-fall-prevention-for-elderly\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What technology is used in fall prevention for the elderly? 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