this post was submitted on 24 May 2026
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The need for a constant signal to scan movement is a good point. Nearby wifi devices can't just be sitting there, they have to be actively transmitting to the router or there's no signal for the target to interfere with. I must have gotten CSI and wifi scanning confused. Tbh I'm not even sure why CSI is in the article except for history, but I found the principle fascinating. In your research did you generate anything like a heat map of a person standing in the room, or do you get something more like a chromatograph or spectrograph than an actual shape?
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.