First of all, thank you very much for your time and help.
This is a very good idea. One of the advantages of this approach is that it is not necessary to perform a labeling process of the timeseries in the dataset. Given my limited experience, I have been looking for an example 1 that I can take as a reference . However, I would have to take into account the following aspects:
1.) Find an elegant way to associate the estimated points to one of the rooms.
2.) This solution allows to estimate points of the trajectory to identify the room through which the person exits. However, I would need to find a way to identify the exit room (origin of the trajectory). I have thought of reversing the sequence of points and estimating the trajectory, with the difference that this time it defines the exit room and not the entrance room.
The other strategy I had thought of is something different. My idea was to label each timeserie with a pair (source room, destination room), normalize the points and orientations and have the network try to find a pattern: “When the first points are near ZONE X and the last points are near ZONE Y, then the classification should be (ZONE X, ZONE Y)”. What do you think?
I apologize for my English, I know it should be much better.