Adaptive user interfaces involves the design of dynamic interfaces, the main purpose of which is to present an adapted alternative to the user to ease the interaction. User’s preferences, context situation, and device’s capabilities help these systems to adapt the interface to make the interaction more adequate to the current situation. Being aware of different characteristics of these entities is vital for reaching the main goals of these systems efficiently. To collect knowledge from these entities, it is necessary to design several formal models to help to organize and give meaning to the gathered data. This article analyzes several literature solutions for modeling users, context, and devices considering different approaches. The article identifies their advantages and drawbacks to finally propose a new ontology model that addresses the identified limitations.

The participation of users within AAL environments is increasing thanks to the capabilities of the current wearable devices. Furthermore, the significance of considering user’s preferences, context conditions and device’s capabilities help smart environments to personalize services and resources for them. Being aware of different characteristics of the entities participating in these situations is vital for reaching the main goals of the corresponding systems efficiently. To collect different information from these entities, it is necessary to design several formal models which help designers to organize and give some meaning to the gathered data. In this paper, we analyze several literature solutions for modeling users, context and devices considering different approaches in the Ambient Assisted Living domain. Besides, we remark different ongoing standardization works in this area. We also discuss the used techniques, modeled characteristics and the advantages and drawbacks of each approach to finally draw several conclusions about the reviewed works.

Healthcare environments, as many other real world environments, present many changing and unpredictable situations. In order to use a social robot in such an environment, the robot has to be prepared to deal with all the changing situations. This paper presents a robot self-configuration approach to overcome suitably the commented problems. The approach is based on the integration of a semantic framework, where a reasoner can take decisions about the configuration of robot services and resources. An ontology has been designed to model the robot and the relevant context information. Besides rules are used to encode human knowledge and serve as policies for the reasoner. The approach has been successfully implemented in a mobile robot, which showed to be more capable of solving situations not pre-designed.