3 ms·
I think ML is completely unnecessary here. In the end it's triangulation of a signal. No need for ML after all. Looks like a classic case of the famous Jurissa
by martin_a 6y ago
I think ML is completely unnecessary here. In the end it's triangulation of a signal. No need for ML after all.
Looks like a classic case of the famous Jurissac Park quote: "Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should."
- fock 6y agoiirc BLE is not that exact and you'd also have to model/measure your sensor position. I think creating a primitive statistical model for looking up the location seems sensible but the ML aspect is definitely a little bit overrated, but I think this thing was never meant to clickbait (it's means to an end available for ~40years now).
- djrogers 6y ago> In the end it's triangulation of a signal. No need for ML after all. Triangulation works accurately in open space, however this use case is inside a home with signals being blocked, bouncing, or attenuated by walls, mirrors, appliances, etc. On top of that, the goal is to use as few BLE rx'ers as possible (meaning not one per room). Given those constraints, a trained ML model actually does make sense.
- eeZah7Ux 6y agoThe pattern emerging from RF reflection across rooms is not completely obvious. "ML" is just a very overstated word for: "I have a handful of example data and a library that does basic math things"