Ambient Intelligence and Immersive Geospatial Visual Analytics

The chapter finally concludes by highlighting a number of open challenges brought by the convergence between GVA and AmI which need to be addressed by the research community in the next future.

System Support for Smart Spaces

There are some important requirements to build effective smart spaces, like human aspects, sensing, activity recognition, context awareness, etc. However, all of them require adequate system support to build systems that work in practice. In this chapter, we discuss system level support services that are necessary to build working smart spaces. We also include a full discussion of system abstractions for pervasive computing taking in account naming, protection, modularity, communication, and programmability issues.

Logical Modeling of Emotions for Ambient Intelligence

Ambient Intelligence (AmI) is the art of designing intelligent and user-focused environments. It is thus of great importance to take human factors into account. In this chapter we especially focus on emotions, that have been proved to be essential in human reasoning and interaction. To this end, we assume that we can take advantage of the results obtained in Artificial Intelligence about the formal modeling of emotions. This chapter specifically aims at showing the interest of logic as a tool to design agents endowed with emotional abilities useful for Ambient Intelligence applications. In particular, we show that modal logics allow the representation of the mental attitudes involved in emotions such as beliefs, goals or ideals. Moreover, we illustrate how modal logics can be used to represent complex emotions (also called self-conscious emotions) involving elaborated forms of reasoning, such as self-attribution of responsibility and counterfactual reasoning. Examples of complex emotions are regret and guilt. We illustrate our logical approach by formalizing some case studies concerning an intelligent house taking care of its inhabitants.

Incorporating Human Aspects in Ambient Intelligence and Smart Environments

In this chapter, we propose to outline the scientific area that addresses Ambient Intelligence applications in which not only sensor data, but also knowledge from the human-directed sciences such as biomedical science, neuroscience, and psychological and social sciences is incorporated. This knowledge enables the environment to perform more in-depth, human-like analyses of the functioning of the observed humans, and to come up with better informed actions. A structured approach to embed human knowledge in Ambient Intelligence applications is presented an illustrated using two examples, one on automated visual attention manipulation, and another on the assessment of the behaviour of a car driver.

Opinion Mining and Information Retrieval

The explosive increase in Internet usage has attracted technologies for automatically mining the user-generated contents (UGC) from Web documents. These UGC-rich resources have raised new opportunities and challenges to carry out the opinion extraction and mining tasks for opinion summaries. The technology of opinion extraction allows users to retrieve and analyze people’s opinions scattered over Web documents. Opinion mining is a process which is concerned with the opinions generated by the consumers about the product. Opinion Mining aims at understanding, extraction and classification of opinions scattered in unstructured text of online resources. The search engines performs well when one wants to know about any product before purchase, but the filtering and analysis of search results often complex and time-consuming. This generated the need of intelligent technologies which could process these unstructured online text documents through automatic classification, concept recognition, text summarization, etc. These tools are based on traditional natural language techniques, statistical analysis, and machine learning techniques. Automatic knowledge extraction over large text collections like Internet has been a challenging task due to many constraints such as needs of large annotated training data, requirement of extensive manual processing of data, and huge amount of domain-specific terms. Ambient Intelligence (AmI) in wed-enabled technologies supports and promotes the intelligent e-commerce services to enable the provision of personalized, self-configurable, and intuitive applications for facilitating UGC knowledge for buying confidence. In this chapter, we will discuss various approaches of Opinion Mining which combines Ambient Intelligence, Natural Language Processing and Machine Learning methods based on textual and grammatical clues.

Context-Sensitive Spatial Interaction and Ambient Control

In this chapter, we first briefly introduce the setting: mobility assistants (the wheelchair Rolland and iWalker) and smart environment control in the Bremen Ambient Assisted Living Lab. In several example scenarios, we then outline our contributions to the state of the art, focussing on spatial knowledge representation, reasoning and spatial interaction (multi-modal, but with special emphasis on natural language dialogue) between three partners: the user, a mobility assistant, and the smart environment.

Home Service Engineering for Sensor Networks

This Chapter deals with the important process related to smart environments engineering, with a specific emphasis on the software infrastructure. In particular, the Chapter focuses on the whole process, from the initial definition of functional requirements to the identification of possible implementation strategies. On the basis of this analysis, a context model as well as the possible choice of relevant sensor types is carried out.

Image-Word Mapping

No Ambient Intelligence can survive without human-computer interactions. Over ninety percent of information in our communication is verbal and visual. The mapping between one-dimensional words and two-dimensional images is a challenge for visual information classification and reconstruction. In this Chapter, we present a model for the image-word two-way mapping process. The model applies specifically to facial identification and facial reconstruction. It accommodates through semantic differential descriptions, analogical and graph-based visual abstraction that allows humans and computers to categorize objects and to provide verbal annotations to the shapes that comprise faces. An image-word mapping interface is designed for efficient facial recognition in massive visual datasets. We demonstrate how a two-way mapping of words and facial shapes is feasible in facial information retrieval and reconstruction.

Possibilities of Ambient Intelligence and Smart Environments in Educational Institutions

The objective of the chapter is to identify and analyze key aspects and possibilities of Ambient Intelligence (AmI) applications in educational processes and institutions (universities), as well as to present a couple of possible visions for these applications. A number of related problems are discussed as well, namely agent-based AmI application architectures. Results of a brief survey among optional users of these applications are presented as well.

Pervasive Computing for Efficient Energy

Increasing energy consumption requires our attention. Resources are exhaustible, so building new power plants is not the only solution. Since residential expenditure is of major parts of overall consumption, concept of intelligent household has potential to participate on energy usage optimization. In this chapter, we concentrate on software methods, which based on inputs gained from an environment monitor, analyze and consequently reduce non-effective energy consumption. We gave a shape to this concept by description of real prototype system called ECM (Energy Consumption Manager). Besides active energy reduction, the ECM system also has an educative function. User-system interaction is designed to teach the user how to use (electric, in case of our prototype) energy effectively. Methods for the analysis are based on artificial intelligence and information systems fields (neural networks, clustering algorithms, rule-based systems, personalization and adaptation of user interface). The system goes further and gains more effectiveness by exchange of data, related to consumption and appliance behaviour, between households.