Artificial intelligence and ambient intelligence

Ambient intelligence (AmI) is intrinsically and thoroughly connected with artificial intelligence (AI). Some even say that it is, in essence, AI in the environment. AI, on the other hand, owes its success to the phenomenal development of the information and communication technologies (ICTs), based on principles such as Moore’s law. In this paper we give an overview of the progress in AI and AmI interconnected with ICT through information-society laws, superintelligence, and several related disciplines, such as multi-agent systems and the Semantic Web, ambient assisted living and e-healthcare, AmI for assisting medical diagnosis, ambient intelligence for e-learning and ambient intelligence for smart cities. Besides a short history and a description of the current state, the frontiers and the future of AmI and AI are also considered in the paper

TaskCBP: an intelligent agent for task planning in elderly care

This paper presents an autonomous intelligent agent developed for healthcare in geriatric residences. The paper focuses on the role of ambient intelligence in the automation of healthcare services. The work here presented shows the development of an autonomous agent, TaskCBP, which incorporates a model of human thinking, such as reasoning based on past experiences. The planning mechanism integrated within the agent has been implemented by means of a novel QSOR neural network. The system has been tested and this paper presents the results obtaine

Patient monitoring under an ambient intelligence setting

Springer - Series Advances in Intelligent and Soft Computing, vol. 72In recent years there has been a growing interest in developing Ambient Intelligence based systems in order to create smart environments for user and environmental monitoring. In fact, higher-level monitoring systems with vital information about the user and the environment around him/her represents an improvement of the quality of care provided. In this paper, we propose an architecture that implements a multi-agent user-profile based system for patient monitoring aimed to improve the assistance and health care provided. This system mixes logical based reasoning mechanisms with context-aware technologies. It is also presented a case based on a scenario developed at a major Portuguese healthcare institution

Using Ubicomp systems for exchanging health information : considering trust and privacy issues

Ambient Intelligence (AmI) and ubiquitous computing allow us to consider a future where computation is embedded into our daily social lives. This vision raises its own important questions and augments the need to understand how people will trust such systems and at the same time achieve and maintain privacy. As a result, we have recently conducted a wide reaching study of people’s attitudes to potential AmI scenarios. This research project investigates the concepts of trust and privacy issues specifically related to the exchange of health, financial, shopping and e-voting information when using AmI system. The method used in the study and findings related to the health scenario will be discussed in this paper and discussed in terms of motivation and social implications

Improving Context-Awareness in a Healthcare Multi-Agent System

Context-aware technologies allow Ambient Assisted Living systems and applications to automatically obtain information from users and their environment in a distributed and ubiquitous way. One of the most important technologies used to provide context-awareness to a system is Wireless Sensor Networks. This paper describes last improvements made on ALZ-MAS, an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and healthcare for Alzheimer patients. In this sense, a new ZigBee platform is used to improve ALZ-MAS. This platform provides the system with new telemonitoring and locating engines that facilitate the integration of context-awareness into it

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results