An Engineering Toolbox to Build Situation Aware Ambient Assisted Living Systems

Abstract Due to increasing anticipated average life and health expenditure ambient assisted living (AAL) systems attract the attention of researchers. To successfully build and deploy AAL systems knowledge from different fields of computer science is needed: pervasive computing to gain the raw data, machine learning and pattern recognition to interpret these data and HCI knowledge to allow implicit interaction with the system. In this paper we propose a reference architecture for building AAL systems. Based on this reference architecture we introduce a toolbox that simplifies the development of AAL systems. The toolbox consists of a meta-model for pipeline systems, a low-level context model, high-level context ontologies, customizable components and tool support

Learning preferences for personalisation in a pervasive environment

With ever increasing accessibility to technological devices, services and applications there is also an increasing burden on the end user to manage and configure such resources. This burden will continue to increase as the vision of pervasive environments, with ubiquitous access to a plethora of resources, continues to become a reality. It is key that appropriate mechanisms to relieve the user of such burdens are developed and provided. These mechanisms include personalisation systems that can adapt resources on behalf of the user in an appropriate way based on the user's current context and goals. The key knowledge base of many personalisation systems is the set of user preferences that indicate what adaptations should be performed under which contextual situations. This thesis investigates the challenges of developing a system that can learn such preferences by monitoring user behaviour within a pervasive environment. Based on the findings of related works and experience from EU project research, several key design requirements for such a system are identified. These requirements are used to drive the design of a system that can learn accurate and up to date preferences for personalisation in a pervasive environment. A standalone prototype of the preference learning system has been developed. In addition the preference learning system has been integrated into a pervasive platform developed through an EU research project. The preference learning system is fully evaluated in terms of its machine learning performance and also its utility in a pervasive environment with real end users

Informative Art Display Metaphors

Abstract. Informative Art display systems have been proposed to provide users with information considered relevant at arbitrary points of work or living engagement, originating from many different –mostly geographically dislocated– sources and presented at the periphery of human (visual) perception. Having the displays operate at the periphery of a user's attention allows other user tasks to sustain primary. Much like the information presented by wallclocks, posters, paintings or windows, peripheral displays move to the center of attention only when appropriate and desirable. Abstract art has been proposed to serve as the visualization paradigm, encoding information into graphical or pictorial artwork by subtly modifying its shape, color and appearance details or its overall impression. This paper approaches a systematic elaboration of visual metaphors able to deliver information via peripheral displays in an aesthetic, artful way. In our approach, the choice of metaphors is driven by the aesthetic appeal of the visual appearance of the display as a whole, out of which certain dynamic emblems or symbols are used to conotate information in a visual style. From experiments we find, that such metaphors are considered by users as a means of personal emotional expression, and that controllable aesthetic attractiveness turns out to be the dominant factor of display appreciation. The choice of aesthetic themes, as well as the control of emblem and symbol dynamics are supported and implemented in our peripheral display framework, a general purpose software framework for informative art display systems