Interesting concept. I did 62 sentences and got "⭐️⭐️⭐️⭐️⭐️" as a rating but none of my last 5 are matched up. I figured it would be pretty easy to pick me out as I use http://mkweb.bcgsc.ca/carpalx/?full_optimization as my main layout and thought that would make my key distances very unique.
On a usability front I echo that the "␣" is definitely confusing, especially next to a ",".
Very interesting! I don't think that I ever had a sample from a non-standard keyboard layout in the dataset.
While it might sound like this should make your samples very different from others, it could actually act the other way and confuse the network. Hopefully, this will be improved in the next version of it, which I'll start training right after this data collection sprint.
I popped a few more in until I hit the maximum 91 stored (either that or it broke). Thanks for fixing the way the space character is displayed.
You could use https://developer.mozilla.org/en-US/docs/Web/API/KeyboardEve... to take advantage of keyboard layout information to more easily classify a user. E.g. if a German user is typing in English their key will register as "KeyY" but their key will register as "z". Even if the neural net just picks up on "non qwerty = weird" I think it would significantly help in it's ability to classify these corner case users.
I look forward to seeing how future versions fare!
On a usability front I echo that the "␣" is definitely confusing, especially next to a ",".