Context is a key issue in interaction between human and computer, describing the surrounding facts that add meaning. In mobile computing research published the parameter location is most often used to approximate context and to implement context-aware applications. We propose that ultra-mobile computing, characterized by devices that are operational and operated while on the move (e.g. PDAs, mobile phones, wearable computers), can significantly benefit from a wider notion of context. To structure the field we introduce a working model for context, discuss mechanisms to acquire context beyond location, and application of context-awareness in ultra-mobile computing. We investigate the utility of sensors for context-awareness and present two prototypical implementations -a light sensitive display and an orientation aware PDA interface. The concept is then extended to a model for sensor fusion to enable more sophisticated context recognition. Based on an implementation of the model an experiment is described and the feasibility of the approach is demonstrated. Further we explore fusion of sensors for acquisition of information on more sophisticated contexts. KeywordsAdaptive User Interface, Context-Awareness, Handheld Computing, Sensor-based UI, Ultra-Mobile Computing, Wearable Computing, IntroductionContext is "that which surrounds, and gives meaning to something else" = . Various areas of computer science have been investigating this concept over the last 40 years, to relate information processing and communication to aspects of the situations in which such processing occurs. Most notably, context is a key concept in Natural Language Processing and more generally in Human-Computer Interaction. For instance, state of the art graphical user interfaces use context to adapt menus to contexts such as user preference and dialogue status. A new domain, in which context currently receives growing attention, is mobile computing. While a first wave of mobile computing was based on portable general-purpose computers and primarily focussed on location transparency, a second wave is now based on ultra-mobile devices and an interest in relating these to their surrounding situation of usage. Ultra-mobile devices are a new class of small mobile computer, defined as computing devices that are operational and operated while on the move, and characterized by a shift from general-purpose computing to task-specific support. Ultra-mobile devices comprise for instance Personal Digital Assistants (PDAs), mobile phones, and wearable computers. A primary concern of context-awareness in mobile computing is awareness of the physical environment surrounding a user and their ultra-mobile device. In recent work, this concern has been addressed by implementation of location-awareness, for instance based on global positioning, or the use of beacons. Location is only one aspect of the physical environment, and as evident from currently reported work location is often used as an approximation of a more complex context. Beyond location, we a...

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Abstract. Mobile information appliances are increasingly used in numerous different situations and locations, setting new requirements to their interaction methods. When the user's situation, place or activity changes, the functionality of the device should adapt to these changes. In this work we propose a layered real-time architecture for this kind of context-aware adaptation based on redundant collections of low-level sensors. Two kinds of sensors are distinguished: physical and logical sensors, which give cues from environment parameters and host information. A prototype board that consists of eight sensors was built for experimentation. The contexts are derived from cues using real-time recognition software, which was constructed after experiments with Kohonen's Self-Organizing Maps and its variants. A personal digital assistant (PDA) and a mobile phone were used with the prototype to demonstrate situational awareness. On the PDA font size and backlight were changed depending on the demonstrated contexts while in mobile phone the active user profile was changed. The experiments have shown that it is feasible to recognize contexts using sensors and that context information can be used to create new interaction metaphors.

In this paper we show how audience expectations towards what is presented on public displays can correlate with their attention towards these displays. Similar to the effect of Banner Blindness on the Web, displays for which users expect uninteresting content (e.g. advertisements) are often ignored. We investigate this effect in two studies. In the first, interviews with 91 users at 11 different public displays revealed that for most public displays, the audience expects boring advertisements and so ignores the displays. This was exemplified by the inclusion of two of our own displays. One, the iDisplay, which showed information for students, was looked at more often than the other (MobiDiC) which showed coupons for shops. In a second study, we conducted repertory grid interviews with 17 users to identify the dimensions that users believe to influence whether they look at public displays. We propose possible solutions to overcome this "Display Blindness" and increase audience attention towards public displays.

Notifications are a core feature of mobile phones. They inform users about a variety of events. Users may take immediate action or ignore them depending on the importance of a notification as well as their current context. The nature of notifications is manifold, applications use them both sparsely and frequently. In this paper we present the first large-scale analysis of mobile notifications with a focus on users' subjective perceptions. We derive a holistic picture of notifications on mobile phones by collecting close to 200 million notifications from more than 40,000 users. Using a data-driven approach, we break down what users like and dislike about notifications. Our results reveal differences in importance of notifications and how users value notifications from messaging apps as well as notifications that include information about people and events. Based on these results we derive a number of findings about the nature of notifications and guidelines to effectively use them.