You can go from this direction but as Red says. It does take extra kit.
Imagine a house owned by a couple that’s a 3*3 grid. (just go with it.). I the house is s main hall and 8 rooms and each room has a door and a motion.
Start by cutting a week into weekdays and weekend and over each of those days cut it into enough time slices to give decent resolution. Then for each day type and day slice you use bayesean math for one bayesean sensor each for a combo of who is expected in the house and each room - so 2 people 9 rooms 18 bayeseans. Each bayesean basically says given all these signals. (motion, TV just turned on, time of day, day part, occupancy signal, the microwave is running etc.) is this person likely to be standing in this room right now with a confidence over X.
Now each of these sensors show the likelihood you’re standing in this room now.
Take the highest of the array for each person.
Now kick back to that motion sensor… When it fires. What’s the bayesean say who did it? When you’re both home it’ll be a little messy but for the most part it’ll resolve more than half of the time and thays before adding BLE.
Add ble into that mix and the accuracy skyrockets because while ble itself is noisy and jumps a lot - the stats smooth everything out. BLE just becomes a very highly weighted signal inthe bayseans array.
If you have math describing the room topology you can also use math (Markov) to weight the adjacent rooms that recently signalled to further filter out impossible travel (you cNt teleport from far bottom left to far top right without travel through connected rooms and physics says thay takes no LESS than x and if you were positive in room z 5 seconds ago these three rooms are still impossible…
(see how this works, you’re stacking a lot of could be and maybe to make damn near impossible to ignore)
Ok you now have a map following you around the house. One more construct.
If you enter a room with one door and the. Close said door. And THEN motion happens… Someone’s in that room permanently until the door opens. This is generally called Wasp-in-a-box. Because your grid. You know the name of the wasp in 60% or better times. You can make it even better still by reverse wasp lockout of any idle room with a door closed. (well he’s not in these take them off the board..)
Stack all these and I’m pretty sure I know where you are on that grid. Template sensor to resolve your person. * object in one of the nine rooms according to that math. I even do a trick where I use the most recent activity and hist stats to weight your preferred rooms at time slice o er the past 10 days…
Complex. Yup.
Takes a lot of setup. No way around it
Accurate. Yup. Very.
This is the basis of how my Room Manager works in ZenOS for my LLM agents.
It works out like this…
(also she’s right btw. That translated to AI assistant for:‘I read your sensor and it’s reporting a low confidence kitchen score because your phone hasn’t settled ina room but holy moly the kitchen is lit up like a Christmas tree…’)