Ambient assisted living (AAL) is focused on providing assistance to people primarily in their natural environment. Over the past decade, the AAL domain has evolved at a fast pace in various directions. The stakeholders of AAL are not only limited to patients, but also include their relatives, social services, health workers, and care agencies. In fact, AAL aims at increasing the life quality of patients, their relatives and the health care providers with a holistic approach. This paper aims at providing a comprehensive overview of the AAL domain, presenting a systematic analysis of over 10 years of relevant literature focusing on the stakeholders' needs, bridging the gap of existing reviews which focused on technologies. The findings of this review clearly show that until now the AAL domain neglects the view of the entire AAL ecosystem. Furthermore, the proposed solutions seem to be tailored more on the basis of the available existing technologies, rather than supporting the various stakeholders' needs. Another major lack that this review is pointing out is a missing adequate evaluation of the various solutions. Finally, it seems that, as the domain of AAL is pretty new, it is still in its incubation phase. Thus, this review calls for moving the AAL domain to a more mature phase with respect to the research approaches

No abstract

No abstract

To develop agent-based systems, one needs a methodology that supports the development process as common in other disciplines. In recent years, several such methodologies and modeling techniques have been suggested. An important question is, to what extent do the existing methodologies address the developers' needs. In this paper we attempt to answer this question. In particular, we discuss suitability of agent modeling techniques to agent-based systems development. In evaluating existing modeling techniques, we address criteria from software engineering as well as characteristics of agent-based systems. Our evaluation shows that some aspects of modeling techniques for agent-based systems may benefit from further enhancements. As we show, these aspects include distribution, concurrency, testing and communication richness. We also find space for (relatively small) improvements in aspects such as the refining of the models throughout the development process and the coverage and consistency checking of the suggested models.

Predicting gully initiation at catchment scale was done previously by integrating a geographical information system (GIS) with physically based models, statistical procedures or with knowledge‐based expert systems. However, the reliability and validity of applying these procedures are still questionable. In this work, a data mining (DM) procedure based on decision trees was applied to identify areas of gully initiation risk. Performance was compared with the analytic hierarchy process (AHP) expert system and with the commonly used topographic threshold (TT) technique. A spatial database was used to test the models, composed of a target variable (presence or absence of initial points) and ten independent environmental, climatic and human‐induced variables. The following findings emerged: using the same input layers, DM provided better predictive ability of gully initiation points than the application of both AHP and TT. The main difference between DM and TT was the very high overestimation inherent in TT. In addition, the minimum slope observed for soil detachment was 2°, whereas in other studies it is 3°. This could be explained by soil resistance, which is substantially lower in agricultural fields, while most studies test unploughed soil. Finally, rainfall intensity events >62.2 mm h‐1 (for a period of 30 min) were found to have a significant effect on gully initiation. Copyright © 2012 John Wiley & Sons, Ltd.