this post was submitted on 24 May 2026
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My topic was fall detection (as in elderly people falling) specifically without using cameras or wearables. The idea was to take the CSI (basically what you see in the image) and just stuff it into some machine learning model to get a prediction as to whether someone fell in a given time frame, so I was trying to classify the signature of the falling "activity". From my literature survey, this has been done successfully with CSI. But as with a lot of research, it typically lacked practicality. Much of my work was implementing the firmware, data recording, processing, and so on. I also had to record a ton of falls (ouch) and label them. I ended up throwing away the CSI approach though, because of the noise reasons I mentioned. That was simply a deal breaker. I went with FMCW radar instead (and it worked pretty good).
Fascinating project! Definitely sounds like at best it might detect that somebody probably fell down, but not that Old Man Jenkins is having a bowl of Lucky Charms instead of Raisin Bran and his blood pressure is a little high - which seems to be the conclusion people are jumping to here.
It definitely depends on the circumstances. With 60 GHz radar e.g. you get quite a good distance resolution and can detect e.g. breathing rate really well (from the torso movement during breathing) and things like how many people are in a room, etc. But its always very dependent on the environment, your settings, subjects, noise, whatever. That's why I said its typically not practical. By using dedicated devices perhaps, and most of these kinds of news are about people who use dedicated devices, but that's like putting a camera in your home. When you have to abuse an existing communication channel, probably not so realistic.