Chorded typing, that is where multiple simultaneous keypresses trigger a single symbol, word or phrase offers unbeatable performance when it comes to typing speed. A trained user can and might even be required to write as fast or faster than human speech. Still it sees virtually no use outside of courtrooms.
Using a 5-finger approach allows the user to operate the mouse simultaneously in a comfortable “FPS-stance.” This has shown to increase UI efficiency for tasks where the work can be done in parallel. In fact to use the left and right hand for different things is very natural for humans.
Unfortunately it is hard for users to memorize all the chords needed. Especially the uncommon ones. Whereas ‘e’ might be a tenth of the language ‘z’ only constitutes a fraction of a percent. On a “Plain old keyboard” a quick look suffices, but the device above offers no such respite. Imagine having to reach for a manual every time you want to spell ‘Zimbabwe’!
Visual feedback contra muscle memory
Most of us have probably used physical keyboards for so long that it is in our very nature. There is very little delay since visual or cognitive input is not required. On the other hand, systems that require visual feedback, say choosing a suggested word on “touch QWERTY” introduces a delay by requiring an increased cognitive load.
Layouts that move dramatically have a disadvantage due to this, that needs to be compensated by other means; say by means of “Reverse Compression”. For more on this, see the Dasher method.
Doing what the keyboard does but better
So what if we always presented a reference on the HUD? Preferably semi transparent and easily hidden and moved as to not inconvenience the user by wasting screen estate.
Since we are doing this on the display we can and should give the users hints as to where to look. This can be done as follows:
- change of fontsize
- brighten probable symbols
- filled / outline
- color coded
As the symbols stay put, we still retain that edge over dynamic systems as after only a little practice the letters that covers 80% of the language will be memorized.
Like what I am doing?
I am working on this full time on a open source basis so please consider supporting me. That way you will see results quicker!
Demo / Prototype
So enough talk and time for a demo! Above you will find the output of a user entering Engl. As such “i” as in “Engl-i-sh”, “a” as in “Engl-a” (Swedish name) or “o” as in “Engl-o-be” lights up. The letter “q” is unlikely so it remains yellow and small.
The prediction is done on a ridiculously small set (wiki page on English) but works ok. Since it works on a character level it can assist with words such as “Englacial”, without having seen it.
A fully featured implementation will support word predictions and OS integration and have the potential of not only being extremely fast, but also portable and powerful.
Thank you for reading!
Do not hesitate to reach out if this interests you! There is a lot to be done and if you wish to help out it would be appreciated.
Please comment, share and or check out my patreon, to help this stay open source and freely available.