Paper Reading #5: A Framework for Robust and Flexible Handling of Inputs with Uncertainty

A Framework for Robust and Flexible Handling of Inputs with Uncertainty


Julia Schwarz, Scott E. Hudson, Jennifer Mankoff, Andrew D. Wilson

Julia Schwarz is a computer science PhD student at Carnegie Mellon University focusing on Human-Computer Interaction.
Scott E. Hudson is a Human-Computer Interaction professor at Carnegie Mellon University.
Jennifer Mankoff is a Human-Computer Interaction professor at Carnegie Mellon University.
Andrew D. Wilson is a researcher at Microsoft who also researched the topic of "Pen + Touch = New Tools."

Summary


In this paper, researchers detailed a new system that would allow for a new kind of input that had some sort of uncertainty factor. As touch screens, gestures, and other "uncertain" inputs develop, there is a  need for some kind of system that accurately predicts what the user means to do. In this system, a dispatcher will sent an event notification to the interactors that have a high selection probability. Based on the selection score, interactors will complete their given action. Sometimes multiple interactors will work at once. When that happens, the possible actions are all sent to the mediator who decides either to run both the actions or to choose one of them to run.

An example they used was three tiny buttons and a user's touch input. The user's touch was mostly over two buttons but one of the buttons was disabled therefore it had a selection score of 0 (the interactor wouldn't fire) and the middle button was correctly activated. They had many other examples that were similar.

One way they tested their system was a case study involving users whose motor skills were impaired. In this test, they had users use a system with normal click events and found that users missed their targets approximately 14% of the time. However, using the probabilistic system, researchers found that the users missed their targets only two times combined.

Discussion


The creation of a system that uses probabilistic input is without a doubt needed and in some cases being used already in consumer products. For example, the Apple iOS on-screen keyboard uses a probabilistic model of what key the user is hit based on touch location but also the number of words that have that letter in it (letters that are used more frequently might have a higher selection score based on previously typed letters).

I definitely believe the researchers proved the hypothesis but not only providing convincing examples but also by using an interesting case study. The wrong input was received only twice with the motor impaired participants. Not only does this have some great ramifications for those who aren't motor impaired, but it also has the potential to bring a better, more reliable system to those who have some motor impairment.