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.
Last week at the ICMI 2013 conference in Sydney, Australia, I presented work done in collaboration with my co-authors Radu-Daniel Vatavu and Jacob O. Wobbrock on new ways of understanding how users make stroke gestures (for example, with stylus and finger on touchscreen devices), through the use of 12 “Relative Accuracy Measures for Stroke Gestures” that our paper introduced. The paper has details on the measures themselves and how they are derived; the talk focuses on what these types of measures can be used for and how they can help us design and build better gesture interactions. For those interested, my presentation slides are available here.
We have also released an open-source toolkit, which we call “GREAT” (Gesture RElative Accuracy Toolkit) that you can use to compute the measures on your own dataset. Download it here!
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!
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!