When someone in your care falls, the question is usually the same: what actually happened, when, and how long were they down? Answering it means scrubbing through footage, often hours of it, from a home camera or a care-home corridor. It is tedious, easy to miss, and stressful when you already suspect something happened.
BGBlur's fall detection is built for that review step. You upload a recorded video, and the AI looks for falls, trips, slips and collapses, then returns timestamps and a description of each event. This guide shows how to use it, what the output looks like, how to protect privacy when you share a clip, and where the limits are. You can try it directly on the BGBlur fall detection page.
TL;DR: the questions people are asking
| Question | Short answer |
|---|---|
| What does it do? | Finds falls, trips, slips and collapses in a recorded video, with timestamps. |
| How? | Upload MP4 or MOV; the fall prompt loads in Video Intelligence. |
| Limits? | Up to 2 GB and 10 minutes per video, per BGBlur. |
| Where is it processed? | In your browser, per BGBlur. |
| Is it a live alarm? | No. Recorded video only. |
| Is it a medical device? | No. |
| Can it be wrong? | Yes: it can miss events or flag non-falls in poor lighting or blocked views. |
What you get back
After you upload a clip, the analysis returns, for each event it believes is a fall:
- A timestamp, so you can jump straight to the moment.
- A description of what occurred.
- What happened afterward: whether the person got up unaided, received help, and roughly how long they stayed down.
- Location and visible triggers, such as a wet floor, a step or a rug edge.
- Whether a mobility aid such as a cane or walker was present.
- An approximate age group (child, young adult, adult, older adult) with a confidence level, not an exact age.
That mix is what makes it more useful than a simple motion alert. A timestamp saves the search, and the context, whether the person recovered alone or needed help, is often what a care manager or family member actually needs to know.
How to use it, step by step
- Pick the right footage. Choose the camera and time window where you think the event happened. Shorter, well-lit clips give better results than a full night of low-light video.
- Trim to the limit. BGBlur lists a limit of 2 GB and 10 minutes for this feature. For longer recordings, split them into 10-minute segments in any video editor and run them in order.
- Open the fall detection page at bgblur.com/en/features/fall-detection and upload your MP4 or MOV.
- Let the analysis run. The fall detection prompt loads automatically in Video Intelligence, the same engine that lets you ask free-form questions about a video.
- Review each flagged timestamp in your own video player. Confirm what you see before acting on the AI's description.
- Ask follow-ups. Because the video is indexed, you can ask questions such as "did anyone enter the room after the fall?" or "how long before someone arrived?" Our notes on Video Intelligence cover how indexing and follow-up questions work, including the credit cost per video minute.
- Blur before you share. If you need to send the clip to family, staff or an insurer, blur faces first with BGBlur's face blur. Our guide to blurring faces in video for privacy walks through that step.
- Keep a record. Save the timestamps, your own notes and the original file, since the AI summary is an aid, not evidence.
Who it is for
Family caregivers who have a camera in a parent's home and want to review an incident without watching hours of footage.
Care-home and assisted-living staff doing incident review after a report, to establish timing and response.
Home-care agencies documenting what happened for supervisors or insurers.
Facilities and security teams reviewing CCTV for slips and falls on a premises, for example in a lobby or a stairwell.
Researchers and product teams evaluating fall-related footage, who need a quick first pass before manual labeling.
It is not designed for sports analytics or for detecting intentional movements such as exercise, though it tries to avoid flagging those as falls.
Accuracy and limits: read this part
BGBlur is candid about what the feature is not, and we think readers should take that seriously.
It is a review tool, not a monitor. The page states it analyzes recorded video you upload and is "not a live alarm, an emergency alert service or a medical device." If you need someone to be alerted at the moment a person falls, you need a different system: a wearable with automatic alerts, a call button, a monitored sensor or in-person care.
It can miss events. BGBlur says it can miss falls or flag non-falls, "especially with poor lighting, long distance or blocked views." A fall behind furniture, in a dark corner or far from the camera is the hardest case.
It can mistake normal movement for a fall. The system tries to separate falls from sitting, bending, exercising or lying down deliberately, but any classifier makes errors, so treat flags as leads to check.
Age estimates are rough. The age group is a visual category with a confidence level, not a measurement. Do not use it for decisions.
We have not benchmarked it. We have not run an independent accuracy test. Before relying on it in a care setting, run a small validation: gather a handful of clips where you know whether a fall occurred (including tricky ones like sitting down hard or kneeling), run them through, and count hits, misses and false alarms.
Privacy and consent
Footage of a person falling is sensitive. Some principles that apply however you do the review:
- Know the rules where you are. Recording in a home is generally the household's decision, but in a care facility, rental property or workplace there are consent, notice and data-protection rules, and they differ by country and state. If you are unsure, ask the facility's compliance contact or a lawyer.
- Prefer local processing. BGBlur says processing happens in your browser and files are not uploaded or stored permanently on its servers. That reduces exposure compared with sending video to a general cloud service, though you should read the current terms and check that your own policy allows this use.
- Minimize who sees the clip. Share only the relevant seconds, blur faces and identifying details, and delete copies when done.
- Respect dignity. A fall is often a vulnerable moment. Use the footage to help the person, not to expose them.
For the related question of how camera-based and camera-free monitoring compare, see our post on Wi-Fi sensing that detects movement without cameras, and for the wider landscape of tools, our guide to AI for elderly care and companion robots, which includes a section on fall detection and safety monitoring.
Choosing between approaches
| Approach | Strength | Weakness |
|---|---|---|
| Review recorded video with AI (this tool) | Cheap and fast for after-the-fact review; gives context | Not real-time; depends on camera coverage |
| Wearable with automatic fall alert | Real-time alert; works anywhere the person goes | Must be worn and charged; false alarms |
| Radar or Wi-Fi sensing in a room | No camera, privacy-friendlier, live | Needs installation; room-specific |
| Camera-based live monitoring | Live visual confirmation | Privacy cost; needs a monitoring service |
| In-person checks | Human judgment | Cost and coverage |
Most real setups combine at least two: a live alert for response and a recorded-video review for understanding what happened. The fall detection page is a good fit for the second.
Practical tips for better results
- Lighting matters. Footage from a lit room works far better than a dark hallway on infrared.
- Camera angle. A camera that sees the whole floor area beats one pointed mostly at a wall.
- Shorter is better. Ten focused minutes will outperform a compressed ten-hour file.
- Know your baseline. Run it once on a clip of normal activity to see what it flags, so you learn its false-positive tendencies.
- Pair with Video Intelligence. For anything unclear, ask a direct question about the moment before the fall, such as what the person was doing.
What this means for what you build or pay
For families and small care providers, the value is time: turning an hour of scrubbing into a few timestamps to check. The cost is credits on Video Intelligence and the discipline to validate and to treat the output as a lead. For anyone building in this space, the lesson is the one in BGBlur's own disclaimer: be explicit about what a tool is not, and put privacy tools such as blur next to the analysis so that sharing footage is safer by default. You can try the feature on the BGBlur fall detection page, and see the other video tools on BGBlur.
Related reading
- Blur faces in video with AI: privacy guide
- How to blur video and image backgrounds with AI
- Blur license plates in video with AI
- AI for elderly care and companion robots
- Wi-Fi sensing with ESP32: movement detection without cameras
- Coral Edge AI platform: a complete guide
Primary: BGBlur fall detection and Video Intelligence feature pages
Details are accurate as of October 7, 2026 and come from BGBlur's feature pages. We have not independently tested accuracy. This is not medical advice, fall detection from recorded video is not a live alarm or a medical device, and you should follow local privacy and consent rules.