So I don't know much about ML; how much better would one expect ML (which I take in this context for 'deep neural network'?) approaches to be compared to a multinomial logistic regression, for applications like this? Is logistic regression old school and/or outdated now?
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."
> 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.
iirc 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).
There are no convincing reasons to consider logistic regression to be outside of ML. ML isnt just deep learning. To me its a collection of mathematical tools that help in designing predictors. This involves stats, optimization, algorithms, stochastic processes, information theory.
The main difference between stats and ml is (i) community, (ii) stress on the details of compute and the focus on prediction accuracy rather than accurate recovery of parameters. A scheme that ensures good prediction even if the parameter is not recovered, for ML that's still a success.
