- Ambient Intelligence (1) (remove)
- Context Modeling in the Domain of Ambient Intelligent Production Environments (2010)
- Ever since Mark Weiser’s vision of Ubiquitous Computing the importance of context has increased in the computer science domain. Future Ambient Intelligent Environments will assist humans in their everyday activities, even without them being constantly aware of it. Objects in such environments will have small computers embedded into them which have the ability to predict human needs from the current context and adapt their behavior accordingly. This vision equally applies to future production environments. In modern factories workers and technical staff members are confronted with a multitude of devices from various manufacturers, all with different user interfaces, interaction concepts and degrees of complexity. Production processes are highly dynamic, whole modules can be exchanged or restructured. Both factors force users to continuously change their mental model of the environment. This complicates their workflows and leads to avoidable user errors or slips in judgement. In an Ambient Intelligent Production Environment these challenges have to be approached. The SmartMote is a universal control device for ambient intelligent production environments like the SmartFactoryKL. It copes with the problems mentioned above by integrating all the user interfaces into a single, holistic and mobile device. Following an automated Model-Based User Interface Development (MBUID) process it generates a fully functional graphical user interface from an abstract task-based description of the environment during run-time. This work introduces an approach to integrating context, namely the user’s location, as an adaptation basis into the MBUID process. A Context Model is specified, which stores location information in a formal and precise way. Connected sensors continuously update the model with new values. The model is complemented by a reasoning component which uses an extensible set of rules. These rules are used to derive more abstract context information from basic sensor data and for providing this information to the MBUID process. The feasibility of the approach is shown by using the example of Interaction Zones, which let developers describe different task models depending on the user’s location. Using the context model to determine when a user enters or leaves a zone, the generator can adapt the graphical user interface accordingly. Context-awareness and the potential to adapt to the current context of use are key requirements of applications in ambient intelligent environments. The approach presented here provides a clear procedure and extension scheme for the consideration of additional context types. As context has significant influence on the overall User Experience, this results not only in a better usefulness, but also in an improved usability of the SmartMote.