Hit It! is a game for the Android platform that is all about speed and quick fingers. You have to touch and move as fast as you can to see if you can beat all levels. The player’s task is to simply touch each appearing circle as fast as possible. The faster they are the more points they get. Players might improve their dexterity by trying to be the fastest guy in the high score.
This game is part of our research about the touch performance on mobile devices and also part of my work as a PhD student. While users play the game we measure where they hit the screen and how fast they are. By combining this information with the position and size of the circles we can estimate how easy each screen position is to touch. Based on this data we are hopefully able to predict user’s performance with different button sizes and positions. We plan to derive an according model and this model could possibly be used to improve the user interface of current smartphones.
We hope that we can collect data from thousands of players. That would enable us to derive information that is valid not only for a small number of people but for every user. We are, however, not interested in you contact list, browsing history, or phone number. Okay – if you are good looking I might be interested in your phone number but I don’t want to collect such data automatically ;). In general we don’t want or need data that enables identifying individuals. Thus, we do not collect those things or other personal information.
Hit It! is available for Android 1.6 and above. You can have a look at users’ comments and the game’s description on AppBrain or install it directly on your Android phone from the Market.
Recently Torben and I spammed the “International Conference on Human-Computer Interaction with Mobile Devices and Services” (better known as MobileHCI) with two papers and a poster about off-screen visualizations. Off-screen visualizations try to reduce the impact of the immanent size restrictions of mobile devices’ display. The idea is that the display is just a window in a larger space. Off-screen visualizations show where the user should look for objects located in this larger space.
The title of the first paper is Visualization of Off-Screen Objects in Mobile Augmented Reality. It deals with displaying points-of-interests using sensor-based mobile augmented reality. We compare the common mini-map that provides a 2D overview about nearby object with the more uncommon visualization of nearby objects using arrows that point at the objects. The images below show both visualizations side-by-side.
To compare the mini-map with the arrows we conducted a small user study in the city centre. We randomly asked passersby to participate in our study (big thanks to my student Manuel who attracted 90% of our female participants). We ended up with 26 people testing both visualizations. Probably because most participants where non tech-savvy guys the collected data is heavily affected by noise. From the results (see the paper for more details) we still conclude that our arrows outperform the mini-map. Even though the study has some flaws I’m quite sure that our results are valid. However, we only tested a very small number of objects and I’m pretty sure that one would get different results for larger number of objects. I would really like to see a study that analyzes a larger number of objects and additional visualizations.
In the paper Evaluation of an Off-Screen Visualization for Magic Lens and Dynamic Peephole Interfaces I compared a dynamic peephole interface with a Magic Lens using an arrow-based off-screen visualization (or no off-screen visualization). The idea of dynamic peephole interfaces is that the mobile phone’s display is a window to a virtual surface. You explore the surface by physically moving your phone around (e.g. a digital map). The Magic Lens is very similar with the important difference that you explore a physical surface (e.g. a paper map) that is augmented with additional information. The concept of the Magic Lens is sketched in the Figures below.
We could measure a difference between the Magic Lens and the dynamic peephole interface. However, we did measure a clear difference between using an off-screen visualization or not. I assume that the impact of those off-screen visualizations has a much larger impact on the user experience than using a Magic Lens or the dynamic peephole. As the Magic Lens relies on a physical surface I doubt that it has a relevant value (for the simple tasked we tested – of course).
As some guys asked me why I use arrows and not those fancy Halos or Wedges (actually I wonder if someone ever fully implemented Wedge for an interactive application) I thought it might be nice to be able to cite my own paper. Thus, I decided to compare some off-screen visualizations techniques for digital maps (e.g. Google maps) on mobile phones. As it would’ve been a bit boring to just repeat the same study conducted by Burigat and co I decided to let users interact with the map (instead of using a static prototype). To make it a bit more interesting (and because I’m lazy) we developed a prototype and published it to the Android Market. We collected some data from users that installed the app and completed an interactive tutorial. The results indicate that arrows are just better than Halos. However, our methodology is flawed and I assume that we haven’t measured what we intended to measure. You can test the application on you Android Phone or just have a look at the poster.
I’m a bit afraid that the papers will end up in the same session. Might be annoying for the audience to see two presentations with the same motivation and similar related work.
As users pan and zoom, display content can disappear into off-screen space, particularly on small-screen devices. The clipping of locations, such as relevant places on a map, can make spatial cognition tasks harder. Halo is a visualization technique that supports spatial cognition by showing users the location of off-screen objects. Halo accomplishes this by surrounding off-screen objects with rings that are just large enough to reach into the border region of the display window. From the portion of the ring that is visible on-screen, users can infer the off-screen location of the object at the center of the ring. We report the results of a user study comparing Halo with an arrow-based visualization technique with respect to four types of map-based route planning tasks. When using the Halo interface, users completed tasks 16-33% faster, while there were no significant differences in error rate for three out of four tasks in our study.
A coupleofotherapproaches try to support similar tasks. We thought testing is better than believing and implemented three different visualization techniques for digital maps on Android. There is a demo app in the market (direct link). We tried to make the whole thing portable but only tested on the G1 and the emulator. I would love to know if it works on other devices like the Motorola Milestone
I removed the app from the market because I lost my keystore and can’t update it anymore. If you are interested in testing it check out the Map Explorer. It is an updated version that you can find in the market.
My student Torben has just published his Android augmented reality app SINLA in the Android market. Our aim is to not only publish a cool app but to also use the market for a user study. The application is similar to Layar and Wikitude but we believe that the small mini-map you find in existing application (the small map you see in the lower right corner in the image below) might not be the best solution to show the users objects that are currently not in the focus of the camera.
Its our first try to use a mobile market to get feedback from real end users. We compare our visualization technique with the more traditional mini-map. We collect only very little information from users at the moment because we’re afraid that we might deter users from providing any feedback at all. However, I’m thrilled to see if we can draw any conclusion from the feedback we get from the applications. I assume that this is a new way to do evaluations which will become more important in the future.
I implemented a markerless object recognition that processes multiple camera images per second on recent mobile phones. The algorithm combines a stripped down SIFT with a scalable vocabulary tree and a simple feature matching.
Based on this algorithm we implemented a simple application which is shown in the video below. The stuff is described in more detail in a paper titled “What is That? Object Recognition from Natural Features on a Mobile Phone” that we submitted to MIRW’09.