A Framework for Robust and Flexible Handling of Inputs with Uncertainty
Julia Schwarz, Scott E. Hudson, Jennifer Mankoff, Andrew D. Wilson
Presented at UIST'10, October 3-6, 2010, New York, New York, USA
Author Bios
- Julia Schwarz is a PhD student at Carnegie Mellon's Human Computer Interaction lab and has interests in handling ambiguous input.
- Scott E. Hudson is a Professor in Carnegie Mellon's Human-Computer Interaction Institute and has published over 100 papers.
- Jennifer Mankoff is an Associate Professor in Carnegie Mellon's Human Computer Interaction lab and holds a PhD from the Georgia Institute of Technology.
- Andrew D. Wilson is a senior researcher at Microsoft Research with a PhD from MIT's Media Lab. He helped found the Surface Computing group.
Summary
Hypothesis
How effective is a new input handling framework that coexists with existing applications and handles uncertainty?
Methods
The authors illustrated the effectiveness of the framework through six case studies. Three focused on improving touch interaction, two on smarter text entry, and one on improved GUI pointing for the motor impaired. The motor impaired test used prerecorded data to simulate clicks in the framework.
Results
Selecting buttons was successful due to the increased target area. Text entry was implemented quickly with no report on its effectiveness. The motor impaired test found a dramatically reduced number of errors compared with the sample's original test. Overall, the framework was effective in handling uncertainty.
Contents
New means of interaction mean that the certainty of inputs is no longer assumable. Conventional input frameworks may resolve uncertainty in an undesirable way. The presented framework can temporarily keep track of possibilities for uncertain input to allow for a more informed decision. Conventional frameworks model inputs, dispatch to an object, interpret what events occurred, and take an action. The interpretations of the proposed framework use a probability mass function to guess what the event was meant to do. Integration with conventional interactors is not yet implemented. A wrapper would be used for this.
Because of ambiguity, each interactor in the possible list, derived by scoring interactors based on a query, receives notification of the event, but conflicting actions must have this ambiguity resolved by making finalization requests to a mediator. The scores are derived from a combination of factors including nearness. For each interactor, the normalized selection score and probability of the event happened are multiplied to determine the final probability of that event on that object. Feedback is immediate for non-permanent or reversible actions even in the case of ambiguity. These actions are modeled separately and are temporary. The mediator can select an action, cancel all actions, or query the user.
Discussion
While the authors claim to have succeeded in creating an effective framework, the data produced was limited to six test cases. Only one of those involved any proper data analysis. While I can't claim that the framework is unlikely to work, the only case in which I was truly convinced was the test of motor impaired clicking, as that provided a measurable claim that the framework was successful. The rest of the cases required me to simply infer from the test with data that they also held.
I found this work interesting because of the rate of erroneous input for touchscreen and other non-traditional input forms is fairly high. With more research into this area, we might be able to use this or a similar framework to make such input forms non-ambiguous and fairly accurate.
When it came to window resizing, I was deeply bothered by the fact that the authors assumed that vertical resizing was unlikely. I work with short/small screens frequently and thus use a vertical resize to increase my productivity. This framework would seemingly punish me for such an action, which is a painful limitation.
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