Ambient Intelligence Applications Research Articles

A novel method for head pose estimation based on the “Vitruvian Man”

In video-surveillance and ambient intelligence applications, head-pose estimation is an important and challenging task. Basically, the problem lies in assessing the pose of the head according to three reference angles, that indicate the head rotation with respect to three orthogonal axes, and are named roll, yaw, and pitch angles. The problem becomes particularly difficult if only 2D video-sequences or still images are available, thus information about the depth of the scene is missing. This makes the computation of the pitch angle very difficult. State-of-the-art methods usually add the information on the pitch angle separately, and this makes them strongly dependent on the hardware used and the scene under surveillance. Moreover, some of them require large training sets with head poses data. Finally, the extraction of several features from the detected face is often necessary. Since head-pose estimation is only a (small) part of a video-surveillance system as a whole, it is necessary to find novel approaches which make the head-pose estimation as simple as possible, in order to allow their use in real-time. In this paper, a novel method for automatic head-pose estimation is presented. This is based on a geometrical model relying on the exploitation of the Vitruvian man’s proportions and the related “Golden Ratio”. Our approach reduces the number of features extracted, avoiding the need for a training set as well as information on the hardware used or the scene under control. Simple ratios among eyes and nose positions, according to the assumed “Golden Ratio”, are used to compute, in particular, the pitch angle. Proposed method performs competitively with respect to state-of-the-art approaches, without requiring their working constraints and assumptions.

Opportunistic control mechanisms for ambience intelligence worlds

Ambient intelligence (AmI) worlds consist of heterogeneous collections of interconnected devices that integrate smoothly in the environment to offer services to their users. These devices can be added, change or fail, modifying the system topology. Also, other circumstances may change, affecting users’ activities, e.g., time, location, or the presence of other users. These changes modify the information the system has available to satisfy users’ needs, i.e., the context. AmI systems need to adapt to these evolving conditions in order to be able to provide their services, but being as unobtrusive as possible for their users. There are also performance requirements that the system must fulfill to provide responses to environment stimuli in real time. Opportunistic control mechanisms address these issues by monitoring the context, and suspending or resolving goals when the appropriate conditions are met. This paper presents FAERIE, a software framework that supports the development of AmI applications with facilities for context management that rely on opportunistic control. The development of FAERIE systems uses a 3D simulator tool for testing and validation called UbikSim. A case study on an artistic installation illustrates the use of this infrastructure.

Otsopack: Lightweight semantic framework for interoperable ambient intelligence applications

In Ambient Intelligence environments machines proactively and transparently work on behalf of humans. The nature of these machines and the communication protocols they use is multifarious. Therefore, the applications running on top of them remarkably demand interoperability. The Triple Space Computing (TSC) paradigm addresses that problem by sharing information represented in a semantic format through a common virtual space. As long as application developers use standard ontologies, different applications using the same spaces will interact automatically. The focus of this paper is to present Otsopack, a fully distributed TSC middleware designed to meet the needs of mobile and resource constrained devices. Otsopack defines a simple HTTP interface for the TSC operations. This interface focuses on simplicity and modularity, so that two implementations that support different modules can still interact. To assess the middleware we provide time and load measurements, and we analyze two independent implementations.

Finding the just-in-time service location and path in a ubiquitous service network

Just-in-time (JIT) is an important topic of lean manufacturing and services, focusing on the reduction of the waiting time, as well as the enhancement of timeliness. In this study, the JIT concept is applied to the application of mobile commerce, ambient intelligence, and ubiquitous computing, which has rarely been explored in the past. As a result, a ubiquitous JIT service network is constructed based on the application of a mobile phone. In this network, a user uploads a request using a mobile phone. The request is then met at the appropriate service location in the network. For the JIT service network, an important issue is how to determine the JIT service location and path. In response to this issue, this study proposes a mathematical programming model to identify the JIT path. Subsequently, a systematic procedure is established to determine the JIT service location. The applicability of the proposed methodology is illustrative with an example.

Sensor fusion in smart camera networks for Ambient Intelligence

This short report introduces the topics of PhD research that was conducted on 2008--2013 and was defended on July 2013. The PhD thesis covers sensor fusion theory, gathers it into a framework with design rules for fusion-friendly design of vision networks, and elaborates on the rules through fusion experiments performed with four distinct applications of Ambient Intelligence.

Context-Aware Multi-Agent Planning in intelligent environments

A system is context-aware if it can extract, interpret and use context information and adapt its functionality to the current context of use. Multi-agent planning generalizes the problem of planning in domains where several agents plan and act together, and share resources, activities, and goals. This contribution presents a practical extension of a formal theoretical model for Context-Aware Multi-Agent Planning based upon an argumentation-based defeasible logic. Our framework, named CAMAP, is implemented on a platform for open multi-agent systems and has been experimentally tested, among others, in applications of ambient intelligence in the field of health-care. CAMAP is based on a multi-agent partial-order planning paradigm in which agents have diverse abilities, use an argumentation-based defeasible contextual reasoning to support their own beliefs and refute the beliefs of the others according to their context knowledge during the plan search process. CAMAP shows to be an adequate approach to tackle ambient intelligence problems as it gathers together in a single framework the ability of planning while it allows agents to put forward arguments that support or argue upon the accuracy, unambiguity and reliability of the context-aware information.

Enhancing Health Care Delivery through Ambient Intelligence Applications

This paper presents the implementation of a smart environment that employs Ambient Intelligence technologies in order to augment a typical hospital room with smart features that assist both patients and medical staff. In this environment various wireless and wired sensor technologies have been integrated, allowing the patient to control the environment and interact with the hospital facilities, while a clinically oriented interface allows for vital sign monitoring. The developed applications are presented both from a patient's and a doctor's perspective, offering different services depending on the user's role. The results of the evaluation process illustrate the need for such a service, leading to important conclusions about the usefulness and crucial role of AmI in health care.

Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments

Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

Tools for the Rapid Prototyping of Provably Correct Ambient Intelligence Applications

Ambient Intelligence technologies have not yet been widely adopted in safety critical scenarios. This principally has been due to fact that acceptable degrees of dependability have not been reached for the applications that rely on such technologies. However, the new critical application domains, like Ambient Assisted Living and Smart Hospitals, which are currently emerging, are increasing the need for methodologies and tools that can improve the reliability of the final systems. This paper presents a middleware architecture for safety critical Ambient Intelligence applications which provides the developer with services for runtime verification. It is now possible to continuously monitor and check the running system against correctness properties defined at design time. Moreover, a visual tool which allows the formal design of several of the characteristics of an Ambient Intelligence application and the automatic generation of setting up parameters and code for the middleware infrastructure is also presented.

Process-in-Network: A Comprehensive Network Processing Approach

A solid and versatile communications platform is very important in modern Ambient Intelligence (AmI) applications, which usually require the transmission of large amounts of multimedia information over a highly heterogeneous network. This article focuses on the concept of Process-in-Network (PIN), which is defined as the possibility that the network processes information as it is being transmitted, and introduces a more comprehensive approach than current network processing technologies. PIN can take advantage of waiting times in queues of routers, idle processing capacity in intermediate nodes, and the information that passes through the network.

Meeting People’s Needs in a Fully Interoperable Domotic Environment

The key idea underlying many Ambient Intelligence (AmI) projects and applications is context awareness, which is based mainly on their capacity to identify users and their locations. The actual computing capacity should remain in the background, in the periphery of our awareness, and should only move to the center if and when necessary. Computing thus becomes ‘invisible’, as it is embedded in the environment and everyday objects. The research project described herein aims to realize an Ambient Intelligence-based environment able to improve users' quality of life by learning their habits and anticipating their needs. This environment is part of an adaptive, context-aware framework designed to make today's incompatible heterogeneous domotic systems fully interoperable, not only for connecting sensors and actuators, but for providing comprehensive connections of devices to users. The solution is a middleware architecture based on open and widely recognized standards capable of abstracting the peculiarities of underlying heterogeneous technologies and enabling them to co-exist and interwork, without however eliminating their differences. At the highest level of this infrastructure, the Ambient Intelligence framework, integrated with the domotic sensors, can enable the system to recognize any unusual or dangerous situations and anticipate health problems or special user needs in a technological living environment, such as a house or a public space.

Combining the real world with simulations for a robust testing of Ambient Intelligence services

This paper proposes a general architecture for testing, validating and verifying Ambient Intelligence (AmI) environments: AmISim. The development of AmI is a very complex task because this technology must often adapt to contextual information as well as unpredictable behaviours and environmental features. The architecture presented deals with AmI applications in order to cover the different components of these kinds of systems: environment, users, context and adaptation. This architecture is the first one that is able to cover all these features, which are needed in a full AmI system. The paper shows that AmISim is able to cover a complete AmI system and to provide a framework which can test scenarios that would be impossible to test in real environments or even with previous simulation approaches. Simulated and real elements coexist in AmISim for a robust testing, validation and verification of the AmI systems, which provide an easier and less costly deployment.

Effect of kinesthetic and tactile haptic feedback on the quality of experience of edutainment applications

Haptic technologies and applications have received enormous attention in the last decade. The incorporation of haptic modality into multimedia applications adds excitement and enjoyment to an application. It also adds a more natural feel to multimedia applications, that otherwise would be limited to vision and audition, by engaging as well the user's sense of touch, giving a more intrinsic feel essential for ambient intelligent applications. However, the improvement of an application's Quality of Experience (QoE) by the addition of haptic feedback is still not completely understood. The research presented in this paper focuses on the effect of haptic feedback and what it potentially adds to the experience of the user as opposed to the traditional visual and auditory feedback. In essence, it investigates certain issues regarding stylus-based haptic education applications and haptic-enhanced entertainment videos. To this end, we used two haptic applications: the haptic handwriting learning tool to experiment with force feedback haptic interaction and the tactile YouTube application for tactile haptic feedback. In both applications, our analysis shows that the addition of haptic feedback will increase the QoE in the absence of fatigue or discomfort for this category of applications. This implies that the incorporation of haptic modality (both force feedback as well as tactile feedback) has positively contributed to the overall QoE for the users.

A real-time network architecture for biometric data delivery in Ambient Intelligence

Ambient Intelligent applications involve the deployment of sensors and hardware devices into an intelligent environment surrounding people, meeting users’ requirements and anticipating their needs (Ambient Intelligence-AmI). Biometrics plays a key role in surveillance and security applications. Fingerprint, iris and voice/speech traits can be acquired by contact, contact-less, and at-a-distance sensors embedded in the environment. Biometric traits transmission and delivery is very critical and it needs real-time transmission network with guaranteed performance and QoS. Wireless networks become suitable for AmI if they are able to satisfy real-time communication and security system requirements. In this paper an hierarchical network architecture, made up of several independent Wireless Automation Cells grouped in Automation Clusters, is presented. The performance evaluation of the proposed architecture, in terms of authentication accuracy and network scheduling efficiency, is also outlined.

Egocentric interaction as a tool for designing ambient ecologies—The case of the easy ADL ecology

The visions of ambient intelligence demand novel interaction paradigms that enable designers and system developers to frame and manage the dynamic and complex interaction between humans and environments populated with physical (real) and virtual (digital) objects of interest. So far, many proposed approaches have adhered to a device-centric stance when including virtual objects into the ambient ecology; a stance inherited from existing interaction paradigms for mobile and stationary interactive devices. In this article, we introduce egocentric interaction as an alternative approach, taking the human agent’s body and mind as the center of reference. We show how this interaction paradigm has influenced both the conception and implementation of the easy ADL ecology, comprising of smart objects, a personal activity-centric middleware attempting to simplify interaction given available resources, ambient intelligence applications aimed at everyday activity support, and a human agent literally in the middle of it all.

A survey of software engineering best practices for the development of smart applications in Ambient Intelligence

Over the past decade, the world of Ambient Intelligence and smart environments has brought us a wide variety of novel applications with potential for exhibiting sophisticated intelligent behavior. These applications are called smart because they can sense, anticipate and adapt themselves to the context, desires and intentions of their users. However, the unpredictability of human behavior, the unanticipated circumstances of execution and a growing heterogeneity of future operational environments impose significant development challenges if these innovative applications are to gain wide acceptance in our daily life. Therefore, applying well-established software engineering methodologies is a fundamental prerequisite for delivering high quality and robust smart software applications. This survey provides a illustrative presentation of software engineering best practices in the area of smart environments and Ambient Intelligence. The aim of this survey is not to explore and compare every possible related work in detail, but to offer insights into the latest developments in this domain and to further the research into the successful design, development and evaluation of Ambient Intelligence frameworks and applications. (This research is partially funded by the Interuniversity Attraction Poles Programme Belgian State, Belgian Science Policy, and by the Research Fund KU Leuven.)

The Design and Implementation of a Hierarchical Networkon-Chip

Network-on-Chip (NoC) architectures represent the most viable solution for on-chip communication issues of future chip multi-processor systems. To meet performance, power and reliability requirements, which characterize future ambient intelligent applications, several NoC architectures were proposed recently. TriBA is an object-oriented chip multi-processor that supports truly parallel execution of objects from hardware. In this paper we design and implement a new router architecture for TriBA network-on-chip. The hardware architecture of the router is detailed and a new routing algorithm is described. We present the packet latency results under different traffic pattern of a 27 core TriBA-NoC model. The results show that TriBA-NoC benefits greatly from traffic locality.

Ranking risks in ambient intelligence projects

At present, numerous ambient intelligent (AmI) applications are emerging which support current electronic and digital environments. Professionals develop each of them by means of projects. AmI application projects have certain features that make them different from other engineering projects. Moreover, a wide rage of risks are present in the whole project. Therefore, to increase these projects’ chances to be successful, it is necessary to manage their specific risks adequately. In order to support the work of those practitioners managing these threats, this research proposes a multicriteria decision-making methodology called Analytic Hierarchy Process. This technique will enable theprioritization of risks in AmI projects according to their level of threat.

Methods for model-based reasoning within agent-based Ambient Intelligence applications

Within agent-based Ambient Intelligence applications agents react to humans based on information obtained by sensoring and their knowledge about human functioning. Appropriate types of reactions depend on the extent to which an agent understands the human and is able to interpret the available information (which is often incomplete, and hence multi-interpretable) in order to create a more complete internal image of the environment, including humans. Such an understanding requires that the agent has knowledge to a certain depth about the human’s physiological and mental processes in the form of an explicitly represented model of the causal and dynamic relations describing these processes. In addition, given such a model representation, the agent needs reasoning methods to derive conclusions from the model and interpret the (partial) information available by sensoring. This paper presents the development of a toolbox that can be used by a modeller to design Ambient Intelligence applications. This toolbox contains a number of model-based reasoning methods and approaches to control such reasoning methods. Formal specifications in an executable temporal format are offered, which allows for simulation of reasoning processes and automated verification of the resulting reasoning traces in a dedicated software environment. A number of such simulation experiments and their formal analysis are described. The main contribution of this paper is that the reasoning methods in the toolbox have the possibility to reason using both quantitative and qualitative aspects in combination with a temporal dimension, and the possibility to perform focused reasoning based upon certain heuristic information.

Towards a Mission-Critical Ambient Intelligent Fire Victims Assistance System

While various ambient computing and intelligence techniques have been used to assist human beings in different aspects of their daily lives and work, this paper investigates potential ambient intelligence support in mission-critical scenarios such as firefighting. The paper reviews state-of-the-art ubiquitous techniques and tools assisting firefighting. Based upon these great research results, the authors then report the design and implementation of an ambient intelligent fire victims assistance application. By sensing the physical environment and occupants in a fire building, the system suggests the safest and fastest route along which the building occupants could evacuate; and when escaping from the building is not possible, the system tries to calm down and inform the trapped ones an action list. The channels to convey the guide assistance include traditional lights, speakers, and occupants’ mobile phones (if existing). The empirical experiments show that ambient intelligence in such a fire response guide can help improve the egress time performance of building occupants. The presented ambient smart fire victims’ assistance system is supposed to work at an early stage of fire in a building. As a complement of existing firefighting techniques, it still faces a number of open questions to be resolved in the future.