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. Adaptive User Interface, Context-Awareness, Handheld Computing, Sensor-based UI, Ultra-Mobile Computing, Wearable Computing, Context 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 argue in favor of awareness of further features that contribute to context, in particular awareness of the physical conditions in a given environment, based on the assumption that the more an ultra-mobile device knows about its usage context, the better it can support their user. With advances in sensor technology, awareness of physical conditions can now be embedded in ultra-mobile devices at low cost. In this paper, we set out with a brief analysis of related work in context-aware mobile computing, followed by introduction of our own working model for context. We will then focus on sensor-based contextawareness and present a prototype that demonstrates the utility of sensor integration in ultra-mobile = Definition of context in The Free On-line Dictionary of Computing 2 devices. We will further motivate integration of multiple sensors and sensor fusion to obtain context information that can not be derived from single sensors. We present a model for sensor fusion, describe the development of a context-awareness device, and report on experiments that investigate sensor-based context-awareness. Context and context-awareness began to be investigated in distributed computing with emergence of mobile computing components in the beginning of the 90s, led by the desire to support computer usage adequately in varying physical environments. Early investigations were carried out at the Olivetti Research Lab with development of the Active Badge system [17], [18] , sensing locality of mobile users to adapt applications to people's whereabouts, and at Xerox PARC with the ubiquitous computing experiment, from which a first general consideration of context-aware mobile computing emerged [14] . Location of use is central to the understanding of context that Schilit et al developed, but their definition of context also includes "the collection of nearby people and objects, as well as changes to those objects over time". A wide range of systems have been reported, that like Active Badge use only location as context describing their physical environment; these includes systems based on the global positioning system (GPS), and distributed systems using the location information available from underlying communication infrastructure, for instance cell information in cellular networks such as GSM. Leonhardt's work on support of locationawareness contributes generic location service based on a formal model of locality [8] . Only few systems have been described that consider context beyond location. Among these are context-aware information capture and retrieval systems that use time in addition to location, for instance for capture of lecture notes as reported in the Classroom2000 study 0, and for note-taking in fieldwork as investigated by [10] . A more general concept for time-and location-aware information capture, retrieval and messaging are Brown's Stick-e-notes, documents tagged with location and time information [3] , which have been applied in fieldwork and for tour guides [2] . Context-aware information retrieval is also investigated in other tour guide applications, for instance Cyberguide at GeorgiaTech [1] and GUIDE in Lancaster [4] . The latter combines the more obvious contexts of location and user preference with an awareness for quality of service provided by the underlying wireless network The use of sensors to obtain context information is common in robotics and machine vision applications. With advances in sensor technology, it is now also affordable to embed sensors in ultra-mobile devices, to obtain information about the physical environment, about the handling of ultra-mobile devices and about the user. Relevant developments in this area are the embedding of sensors in handheld computers, the development of sensor devices as independent components, and the integration of sensors in wearable computing systems. In the area of handheld computers, sensor integration is primarily employed for novel interaction techniques. ; simple audio sensing has been employed to obtain an awareness of whether the user is interruptible [13] ; and environment sensors have been used to support fieldworkers in mobile workplaces, for awareness of co-located objects and their state [7] . A few key observations can be derived from the analysis of work on context-aware mobile computing. First, the utility of context and context-awareness in mobile computing is evident from reported work, but approaches are hard to relate for lack of a general understanding of what context is. Second, the use of location is dominant but often in approximation of a more general context, for instance guide tours establish the surrounding context indirectly based on location sensing. Third, sensors can be employed to advance context-awareness beyond location-awareness, but obtained context tends to be either of low ¶ www.media.mit.edu/affect/ 3 abstraction (e.g. noise level, temperature, ...) or carefully crafted for specific applications (e.g. user's level of attention).
The usefulness of context in wearable computing in general is e.g. discussed in REF .
There is more to context than location
Computers & Graphics
Computer Science