Disabled people can benefit greatly from assistive digital technologies. However, this increased human-machine symbiosis makes it important that systems are personalized and transparent to users. Existing work often uses data-oriented approaches. However, these approaches lack transparency and make it hard to influence the system’s behavior. In this paper, we use knowledge-based techniques for personalization, introducing the concept of Semantic User Models for representing the behavior, values and capabilities of users. To allow the system to construct such a user model, we investigate the use of a conversational agent which can elicit the relevant information from users through dialogue. A conversational interface is essential for our case study of navigation support for visually impaired people, but in general, has the potential to enhance transparency as users know what the system represents about them. For such a dialogue to be effective, it is crucial that the user understands what the conversational agent is asking, i.e., that misalignments that decrease the transparency are avoided or resolved. In this paper, we investigate whether we can use a conversational agent for Semantic User Model elicitation, which types of misalignments can occur in this process and how they are related, and how misalignments can be reduced. We investigate this in two (iterative) qualitative studies (n = 7 & n = 8) with visually impaired people in which a personalized user model for navigation support is elicited via a dialogue with a conversational agent. Our results show four hierarchically structured levels of human-agent misalignment. We identify several design solutions for reducing misalignments, which point to the need for restricting the generic user model to what is needed in the domain under consideration. With this research, we lay a foundation for conversational agents capable of eliciting Semantic User Models.

Navigation in urban environments is very challenging for blind pedestrians.Although many navigation approaches using various principles or sensors to help visually impaired people exists nowadays they still have problems to navigate in complex buildings, find entrances to buildings or to navigate to correct public transport stops.Current solutions use a large number of sensor that needs to be installed in the environment needed to track every single move of the user.We present a solution to reduce the number of installed sensors by using previously developed set of landmark-enhanced navigation instructions allowing us to lower the necessary number of Bluetooth beacons by using them only for proximity notification at indoor decision points, indicating public transport station and entrances.The evaluation in the field study (N = 8) suggests a good potential of the approach, especially in terms of usability, recovery from going astray and beacon deployment cost.Further, we provide guidance on beacons placement in the environment.

Visual impairment limits a person mainly in ability to move freely and independently.Even with many navigation aids and tools currently on the market, almost one third of the visually impaired do not travel independently without a guide, and human-prepared landmark-enhanced itineraries of the route are the most useful.We designed a system which based on a specific efficiently collected geographical data generates human-like landmark-enhanced navigation instructions.The studies we conducted (quantitative n = 16, qualitative n = 6) proved usability and efficiency of the system.Further we provide set of design recommendations to increase the usability of the system along with specific examples of usage with particular landmarks.

We present an external labeling laid over small and/or overlapping 2D objects as an efficient representation for their selection. The approximation of objects with points allows us to transform the labeling problem to graph layout problem, which we solve by means of force-based algorithm. The input parameters allow us to influence the resulting layout of label boxes (e.g. to adapt their distance for imprecise input devices). In a study with 15 participants two implementations of our algorithm were compared against labeling method, where all label boxes share the same offset from corresponding objects. The results of the study show that implementation using a special functionality (temporary freezing of the label box position recalculation) was 14% faster with a comparable accuracy. The subjective evaluation revealed that the implementation with temporary freezing is perceived as most comfortable, fastest and most accurate. The implementation without temporary freezing showed much higher error rate and cannot be recommended.