Category Archives: Software / Data

MTAGIC project releases UI Design Guidelines app!

The MTAGIC Project, which is studying differences in how children and adults interact with touchscreen devices, has released a new open-source app to help developers implement the design recommendations we included in our research papers. Based on findings from our studies of children and adults using mobile touchscreen devices, we found that children have more difficulty successfully acquiring touch targets and making consistent gestures than adults do. We developed recommendations for how to design touchscreen interfaces to increase children’s success, and those are demonstrated in a handy Android app illustrating how to integrate the design recommendations into your own apps. Check out screenshots, a video demo, and the source code itself for the app here.

If you use this app in your own apps or in your research, we want to hear about it! Drop us a line or post a comment here! Of course, citations to the design recommendations we make in our papers are always welcome as well.

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C# implementation of $P recognizer available, online demo in JavaScript!

We have recently made available a reference implementation of our $P recognizer in C#, which you can find on the $P project page. This version augments our original online demo and implementation in JavaScript, both still available as well. When you download the C# .zip file, you receive (1) a DLL of just the recognizer which you can use in your C# applications, (2) a canvas drawing and recognizing demo in C# equivalent to the online JavaScript demo, and (3) a “How To” document explaining how to incorporate these versions in your own projects. Try it out and let us know how it goes!

A reminder: if you implement $P in a new language or in a new way, feel free to let us know and we will link to it from our page as well! Don’t forget to cite us!

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New $N multistroke gesture set released!

I am pleased to announce that the Mixed Multistroke Gestures (MMG) dataset from our GI 2012 paper is now publicly available for download! It contains samples from 20 people who entered each of 16 gesture types 10 times, using either their finger or a stylus on a Tablet PC, at three different speeds (slow, medium, fast), for a total of 9600 samples. The samples are stored in the $N Recognizer‘s data format, and each person’s samples are separated into user-speed sub-folders. See more details on the gestures, the users who entered them, and $N’s accuracy in recognizing them in our GI 2012 paper. You may download the dataset here. If you use it in your work, please cite us!

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