Ambient Intelligence Research Research Articles

Ambient intelligence: Technologies and applications

Ambient intelligence (AmI) is an emerging discipline that brings intelligence to our everyday environments and makes those environments sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence (AI). It is because these contributing fields have experienced tremendous growth in the last few years, ambient intelligence (AmI) research has strengthened and expanded. It is because ambient intelligence (AmI) research is maturing, the resulting technologies promise to revolutionize daily human life by making people’s surroundings flexible and adaptive. However, Ambient Intelligent (AmI) services may assist us both reactively (through user-initiated requests) and proactively (with AI-initiated help). In other words, Ambient Intelligence (AmI) does not ask the user what they require, but rather has the power to make judgments and take action based on user context. Smart Homes is one example of such systems but the idea can also be used in relation to hospitals, public transport, factories and other environments. The achievement of Ambient Intelligence (AmI) largely depends on the technology deployed (sensors and devices interconnected through networks) as well as on the intelligence of the software used for decision-making. The aim of this research work is to discuss the concept of ambient intelligent (AmI), applications, its architectural components, and benefits. This research may also highlight challenges and opportunities that Ambient Intelligence (AmI) researchers will face in the coming years.

Grand challenges for ambient intelligence and implications for design contexts and smart societies

This paper highlights selected grand challenges that concern especially the social and the design dimensions of research and development in Ambient Intelligence (AmI) and Smart Environments (SmE). Due to the increasing deployment and usage of ‘smart’ technologies determining a wide range of everyday life activities, there is an urgent need to reconsider their societal implications and how to address these implications with appropriate design methods. The paper presents four perspectives on the subject grounded in different approaches. First, introducing and reflecting on the implications of the ‘smart-everything’ paradigm, the resulting design trade-offs and their application to smart cities. Second, discussing the potential of non-verbal communication for informing the design of spatial interfaces for AmI design practices. Third, reflecting on the role of new data categories such as ‘future data’ and the role of uncertainty and their implications for the next generation of AmI environments. Finally, debating the merits and shortfalls of the world’s largest professional engineering community effort to craft a global standards body on ethically aligned design for autonomous and intelligent systems. The paper benefits from taking different perspectives on common issues, provides commonalities and relationships between them and provides anchor points for important challenges in the field of ambient intelligence.

Ambient context-based modeling for health risk assessment using deep neural network

Context computing is a branch of ambient intelligence (AmI) research, which has been rapidly emerging in the support of intelligent smart health platform solution. To develop reliable ambient computing using the hybrid peer-to-peer network and Internet of Things, machine learning, deep learning, artificial intelligence, and context awareness have been applied. This study proposes an ambient context-based modeling for a health risk assessment using deep neural network. In the proposed method, we collected medical information from chronic disease patients such as EMR, PHR, and medical histories, as well as environmental data from a health platform. Subsequently, heterogeneous data are integrated through selecting, cleaning, modeling, and evaluating the collected raw data and then the context is created. The structured input data such as a sensor data are normalized by transforming the time domain data to the frequency domain information. Using a deep neural network, the normalized data are applied to create an ambient context. A deep neural network is composed of the following three layers: input layers with treated and untreated data; hidden layers where connection strength is trained as a weight; and output layers of trained results. In the deep neural network layers, the control of the weight of training data enables repeated learning to create an ambient context pattern. Using an ontology inference engine, unstructured/structured data, including individual health data and environmental information, and their context is presented as ontology metadata. In the knowledge base, hidden association relationships are discovered through mining. To inform the individual health conditions exposed to the individual environmental contexts, a health risk assessment model is developed with a set of the ambient context pattern learned with metadata and a deep neural network. The Minkowski distance formula, which defines a normalized geometrical distance between two nodes, is used to measure the similarity between the patients with chronical disease and the individual user based on the context. In the proposed model, the risk is represented as a similarity-based index. The risk assessment model can be implemented into the individual risk alert/prevention system. The model may significantly impact the healthcare industry as well as ambient intelligence research, thus contributing to improve the quality of human life of the future society.

Activity inference engine for real-time cognitive assistance in smart environments

Recent research in ambient intelligence allows wireless sensor networks to perceive environmental states and their changes in smart environments. An intelligent living environment could not only provide better interactions with its ambiance, inside electrical devices and everyday objects, but also offer smart services, even smart assistance to disabled or elderly people when necessary. This paper proposes a new inference engine based on the formal concept analysis to achieve activity prediction and recognition, even abnormal behavioral pattern detection for ambient-assisted living. According to occupants’ historical data, we explore useful frequent patterns to guide future prediction, recognition and detection tasks. Like the way of human reasoning, the engine could incrementally infer the most probable activity according to successive observations. Furthermore, we propose a hierarchical clustering approach to merge activities according to their semantic similarities. As an optimized knowledge discovery approach in hierarchical ambient intelligence environments, it could optimize the prediction accuracies at the earliest stages when only a few observations are available.

Training Engineers for the Ambient Intelligence Challenge

The increasing complexity of the new breed of distributed intelligent systems, such as the Internet of Things (IoT), which require a diversity of languages and protocols, can only be tamed with design and programming best practices. Interest is also growing for including the human factor, as advocated by the Ambient Intelligence (AmI) research field, whose focus is on transparently and intelligently supporting people. These new design methodologies are increasingly needed in the toolbox of new electronic and computer engineers, and teaching strategies should be devised that allow students to acquire a systems-level view instead of getting lost in technology-oriented approaches. This paper describes a study carried out over two academic years, in a course in ambient intelligence at Politecnico di Torino, Italy. In the course, a project-based learning approach was adopted, in which students design and prototype an AmI system, and their progress is closely monitored throughout the semester. The paper presents the learning goals and teaching strategies, analyzes the learning outcomes from the qualitative and quantitative points of view, and highlights the lessons learned in the process

Ambient Intelligence: Vision, research, and life

This paper evaluates the progress in the field of Ambient Intelligence since its early days. It discusses the extent to which earlier visions of Ambient Intelligence have materialized arguing that to a large extent we already live in an era of Ambient Intelligence, despite that ‘true intelligence’ appears to remain elusive. We examine the extent to which this is so, focusing on shifting mind-sets and ambitions for researchers in the last years. We discuss the various reincarnations of the vision from a historical and societal perspective, together with some of the critiques leveled against it. With many research challenges still remaining open, we discuss how Ambient Intelligence research can be aligned to contemporary societal needs. Particularly important is to create mechanisms that will enable people to interact and benefit from the envisioned technological infrastructure avoiding some of the dangers that embedding computational intelligence in our physical environment brings about. Observing the diversification of research to address different application areas, we note how the impetus towards Ambient Intelligence needs to be renewed with a uniting challenge that transcends the development of technological infrastructure.

Building knowledge-based systems to enable ambient social interactions

In this paper, we identify ambient social interactions as an important direction of research in Ambient Intelligence. To enable intelligence for ambient social interactions, we build knowledge-based systems to aggregate information from heterogeneous sources, including physical environments, users' social tasks and available services. Such knowledge-based systems enable intelligence not only for individuals, but also for user groups. We propose a general framework for aggregating information from different sources. A novel representation, Entity Notation EN, is suggested as a starting point for the purpose of connecting all information to knowledge-based systems, which, in its turn, offers good possibilities to support Semantic Web technologies for development of smart functions for ambient social interactions. To demonstrate the usability of our framework and representation, we present an implementation of a map reminder service, and report analysis based of it.

Behavior-based adaptive call predictor

Predicting future calls can be the next advanced feature of the next-generation telecommunication networks as the service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedules, avoiding unwanted communications (e.g. voice spam), and resource planning in call centers. Predicting calls is a very challenging task. We believe that this is an emerging area of research in ambient intelligence where the electronic devices are sensitive and responsive to people's needs and behavior. In particular, we believe that the results of this research will lead to higher productivity and quality of life. In this article, we present a Call Predictor (CP) that offers two new advanced features for the next-generation phones namely “Incoming Call Forecast” and “Intelligent Address Book.” For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller's behavior and reciprocity. For the Intelligent Address Book, the CP generates a list of most likely contacts/numbers to be dialed at any given time based on the user's behavior and reciprocity. The CP consists of two major components: Probability Estimator (PE) and Trend Detector (TD). The PE computes the probability of receiving/initiating a call based on the caller/user's calling behavior and reciprocity. We show that the recent trend of the caller/user's calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. The TD detects the recent trend of the caller/user's calling pattern and computes the adequacy of historical data in terms of reversed time (time that runs towards the past) based on a trace distance. The recent behavior detection mechanism allows CP to adapt its computation in response to the new calling behaviors. Therefore, CP is adaptive to the recent behavior. For our analysis, we use the real-life call logs of 94 mobile phone users over nine months, which were collected by the Reality Mining Project group at MIT. The performance of the CP is validated for two months based on seven months of training data. The experimental results show that the CP performs reasonably well as an incoming call predictor (Incoming Call Forecast) with false positive rate of 8%, false negative rate of 1%, and error rate of 9%, and as an outgoing call predictor (Intelligent Address Book) with the accuracy of 70% when the list has five entries. The functionality of the CP can be useful in assisting its user in carrying out everyday life activities such as scheduling daily plans by using the Incoming Call Forecast, and saving time from searching for the phone number in a typically lengthy contact book by using the Intelligent Address Book. Furthermore, we describe other useful applications of CP besides its own aforementioned features including Call Firewall and Call Reminder.

Social interactions in Ambient Intelligent environments

This thesis is focussing on research into Ambient Intelligence. More specific, the thesis will focus on infrastructures, methodology and applications deployed in Ambient Intelligence research. By presenting work on those three aspects the thesis contributes to the scientific research field of Ambient Intelligence research. It should be emphasized that this does not imply that all Ambient Inteligence related research should be done within the presented infrastructures and with the presented methodology. The applications of Ambient Intelligence presented in this thesis investigate the possible role of technology to support people in maintaining social connectedness. More specific, a series of empirical studies is reported that explore the potential for Ambient Intelligent environments to support and facilitate social affiliation through technology. By positioning the human need in the center of technology development, Ambient Intelligence (described in chapter 2) is an attractive approach to application development for social affiliation. The studies reported in this thesis have in large part been conducted in the ExperienceLabs at Philips Research. These test facilities (described in chapter 4) are specifically suitable for experience research (described and illustrated with a case study in chapter 3) that requires controlled yet realistic testing environments. In a first series of experiments with awareness systems to mediate presence through silhouette representations of remote activities is investigated (see chapter 5) and mediating availability through presence information (see chapter 6). The results of these studies show that providing simple presence information can create strong feelings of social connectedness while supporting the function of availability judgements (i.e. using the presence information to estimate the availability of the remote person to engage into a conversation). Taking Ambient Intelligence (AmI) to the next level, the role of social intelligence (see the extended AmI model in chapter 2) is investigated in a controlled laboratory study and discussed in chapter 7. From this study it is learned that equipping technical systems with a level of social intelligence results in enriched and human-like interactions with the system. The scientific contribution of this thesis is in: ? A Methodological Framework for Ambient Intelligent research ? The empirical validation of Experience and Application Research Centers as scientific instruments ? Demonstration of the affective benefits of Awareness Systems ? Illustration of how social intelligence can be manifested in a Home Dialogue System ? The Assessment of Social Intelligence in a Home Dialogue System ? Illustrated the spillover effects of Social Intelligence

What would you ask to your home if it were intelligent? Exploring user expectations about next-generation homes

Ambient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications; however a clear discrepancy between current installations and research-level designs can be easily noticed. Whether this gap is due to the natural distance between research and engineered applications or to mismatching of needs and solutions remains to be understood. This paper discusses the results of a survey about user expectations with respect to intelligent homes. Starting from a very simple and open question about what users would ask to their intelligent homes, we derived user perceptions about what intelligent homes can do, and we analyzed to what extent current research solutions, as well as commercially available systems, address these emerging needs. Interestingly, most user concerns about smart homes involve comfort and household tasks and most of them can be currently addressed by existing commercial systems, or by suitable combinations of them. A clear trend emerges from the poll findings: the technical gap between user expectations and current solutions is actually narrower and easier to bridge than it may appear, but users perceive this gap as wide and limiting, thus requiring the AmI community to establish a more effective communication with final users, with an increased attention to real-world deployment.

Spatio-Temporal Footprints

The recognition of human behaviour from sensor observations is an important area of research in smart homes and ambient intelligence. In this paper, we introduce the idea of spatio-temporal footprints, which are local patterns in space and time that should be similar across repeated occurrences of the same behaviour. We discuss the spatial and temporal mapping requirements of these footprints, together with how they may be used.

Multi-agent smart environments

Research in pervasive computing and ambient intelligence has catapulted smart environments to the forefront of many research labs. While tremendous strides have been made toward realizing the dream of designing environments that act as intelligent agents, the size of the problem creates issues that still need to be addressed by researchers. One such issue is the role of multi-agent systems in smart environments. In this article we discuss the role of multi-agent research in the context of smart environments and survey current research in the area. In addition, we pose several challenges to be tackled in this dynamic and complex area of research.

Google home: Experience, support and re-experience of social home activities

Ambient intelligence research is about ubiquitous computing and about social and intelligent properties of computer-supported environments. These properties aim at providing inhabitants or visitors of ambient intelligence environments with support in their activities. Activities include interactions between inhabitants and between inhabitants and (semi-) autonomous agents, including mobile robots, virtual humans and other smart objects in the environment. Providing real-time support requires understanding of behavior and activities. Clearly, being able to provide real-time support also allows us to provide off-line support, that is, intelligent off-line retrieval, summarizing, browsing and even replay, possibly in a transformed way, of stored information. Real-time remote access to these computer-supported environments also allows participation in activities and such participation as well can profit from the real-time capturing and interpretation of behavior and activities performed and supported by ambient intelligence technology. In this paper, we illustrate and support these observations by looking at results obtained in several European and US projects, in particular projects on smart environments, whether they are smart meetings or lecture rooms, smart offices or intelligently monitored events in public spaces. In particular, we look at the augmented multi-party interaction (AMI) project in which we are involved and we try to sketch a framework in which we can transform research results from the meeting context to the home environment context.

Ambient multimodality: towards advancing computer accessibility and assisted living

The aim of this paper is to point out the benefits that can be derived from research advances while implementing concepts such as ambient intelligence and ubiquitous/pervasive computing for promoting universal access in the information society, that is, for contributing to enable everybody, especially people with physical disabilities, to have easy access to all computing resources and information services that the upcoming worldwide information society will soon make available to the general public as well as to expert users. Following definitions of basic concepts relating to multimodal interaction, the significant contribution of multimodality to developing universal access is briefly discussed. Then, a short state of the art in ambient intelligence research is presented, including references to some major research projects either in progress or recently completed. The last section is devoted to bringing out the potential contribution of advances in ambient intelligence research and technology to the improvement of computer access for physically disabled people, hence, to the implementation of universal access. This claim is mainly supported by the following observations: (1) most projects are aiming at implementing ambient intelligence focus research efforts on the design of new interaction modalities and flexible/adaptive multimodal user interfaces; outcomes of these projects may contribute to improving computer access for users with physical disabilities; (2) such projects target applications meant to support users in a wide range of daily activities which will have to be performed simultaneously with the related supporting computing tasks; therefore, users will be placed in situational contexts where they will be confronted with similar difficulties to those encountered by physically disabled users, since they will have to share out their perceptual and motor capabilities between one or several daily activities and the computing tasks meant to support them; (3) ambient intelligence applications being intended for the general public, a wide range of interaction devices supporting a great variety of input and output modalities will be available, making it possible to provide physically disabled users with appropriate human–computer interaction means tailored to their individual needs at a reasonable expense.

The dark side of ambient intelligence

PurposeTo survey ambient intelligence research in Europe, the USA and Japan and, in particular, in the context of the issues of privacy, identity, security and trust and the safeguards proposed to protect them.Design/methodology/approachThis paper is based on research being conducted by the SWAMI consortium under the EC's Sixth Framework Programme. SWAMI stands for Safeguards in a World of Ambient Intelligence. The consortium comprises five partners: the Fraunhofer Institute (Germany), the Technical Research Center of Finland (VTT Electronics), Vrije Universiteit Brussel (Belgium), the Institute for Prospective Technological Studies (IPTS) (Spain) and Trilateral Research & Consulting (UK). The 18‐month SWAMI project began in February 2005.FindingsMost AmI projects do not take into account privacy, security and related issues. However, a reasonable number do (perhaps a quarter of those in Europe) to a greater or lesser extent and some have proposed safeguards.Research limitations/implicationsThis paper references only a limited set of the research projects being carried out in Europe, the USA and Japan. More detailed information can be found on the SWAMI web site (http://swami.jrc.es).Practical implicationsA mix of different safeguards will be needed to adequately protect privacy, etc. in the new world of AmI.Originality/valueUntil now, there has been no reasonably comprehensive survey of AmI research projects in Europe, the USA and Canada focused on privacy, security, identity and trust issues. None has considered the range of safeguards needed to protect privacy, etc.