Blog Paper Reading #5

A framework for robust and flexible handling of inputs with uncertainty

Authors: 

Julia Schwarz - Carnegie Mellon University, Pittsburgh, PA, USA

Scott Hudson - Carnegie Mellon University, Pittsburgh, PA, USA

Jennifer Mankoff - Carnegie Mellon University, Pittsburgh, PA, USA

Andrew D. Wilson - Microsoft Research, Redmond, WA, USA

Released: UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology

Summary
There can exist ambiguity in handling gestures. Two gestures may start in the same place, but end differently, and choosing one over the other at the beginning of an input would be a mistake. This is where the concept of interactors comes in. A gesture can trigger two separate invisible analyses (interactors) and not act until the underlying framework is sure that only one or no action has taken place.

Hypothesis

The researchers hypothesized that by treating their input in a way that handles its true uncertainty well, they could increase the accuracy of user interactions.


Methods
The way probability was assigned for each interactor was done by using a probability mass function. All probabilities added up to 100% where 80% represents a successful match. Six examples are shown described.

• Smart window resizing - A touch input starts at the intersection of an icon in the background and the edge of a window. The gesture could mean the user wants to either move the icon or resize the window. The difference is determined by having the probability of resizing be larger if the input moves perpendicular to the window. However, if another direction is taken, it is more likely the user wanted to move the icon instead.
• Remote sliders - Two sliders exist parallel to each other, and a touch input is generated directly between the two. Either slider has the same probability, but when the touch is moved up or down closer to a single slider, the probability function decides the closer slider was intended.
• Tiny buttons - Three buttons which are all smaller than the size of a single touch input exist. If a touch is generated slightly to the left, and if the left button is disabled, it is improbable that the left button was desired and the middle one would receive the input.
• Smart Text Delivery - If a user does not select a text box before typing, the probabilities of a finalization can help determine the correct text box
• Using the metrics from a speech recognition API, the same probability based decision process can be applied to speech
• The largest test was for users with impaired mobility. The amount of uncertainty in this case is much more than a normal user. 

Results

The examples shown are the end result of the framework in question. They work.

Discussion
Many of these examples can be shown already in touch based interfaces for end users. However, being specified and quantified in a framework is useful for future development theory. This paper is interesting to me because I have used touch interfaces that do not account for uncertainty, and those that do. It is clear that the ones that do are superior in a user preference standpoint. These topics will most always have to be considered in touch interfaces.