Journal of Ambient Intelligence and Smart Environments

Current smart-home and automation systems have reduced generality and modularity, thus confining users in terms of functionality. This paper proposes a novel system architecture and describes the implementation of a user-centric smart-home gateway that is able to support home-automation, energy usage management and reduction, as well as smart-grid operations. This is enabled through a middleware service that exposes a control API, allowing the manipulation of the home network devices and information, irrespectively of the involved technologies. Additionally, the system places the users as the prime owners of their data, which in turn is expected to make them much more willing to install and cooperate with the system. The gateway is supported by a centralised user-centric machine-learning component that is able to extract behavioural patterns of the users and feed them back to the gateway. The results presented in this paper demonstrate the efficient operation of the gateway and examine two well-know machine learning algorithms for identifying patterns in the user’s energy consumption behaviour. This feature could be utilised to improve its performance and even identify energy saving opportunities.

The vacuum cleaner robot requires the artificial intelligence to solve the problem of sweeping of the entire environment areas taking into account some factors such as the time and the length of the generated path. This task is known as the complete region coverage navigation (CRCN). In this paper, to resolve the problem of CRCN in a room environment, we propose the pulse-coupled neural network (PCNN) model. The latter is based on the firing event in which pulses are emitted from a neuron to another until it fires all the neurons. Each neuron has only local lateral connections with its neighbors. In addition, this mechanism helps the robot to pass through every part of the dynamic environment by avoiding obstacles using different sensors. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.

Today, privacy is a key concept. It is also one which is rapidly evolving with technological advances, and there is no consensus on a single definition for it. In fact, the concept of privacy has been defined in many different ways, ranging from the “right to be left alone” to being a “commodity” that can be bought and sold. In the same time, powerful Ambient Intelligence (AmI) systems are being developed, that deploy context-aware, personalised, adaptive and anticipatory services. In such systems personal data is vastly collected, stored, and distributed, making privacy preservation a critical issue. The human-centred focus of AmI systems has prompted the introduction of new kinds of technologies, e.g. Privacy Enhancing Technologies (PET), and methodologies, e.g. Privacy by Design (PbD), whereby privacy concerns are included in the design of the system. One particular application field, where privacy preservation is of critical importance is Ambient Assisted Living (AAL). Emerging from the continuous increase of the ageing population, AAL focuses on intelligent systems of assistance for a better, healthier and safer life in their living environment. In this paper, we first build on our previous work, in which we introduced a new tripartite categorisation of privacy as a right, an enabler, and a commodity. Second, we highlight the specific privacy issues raised in AAL. Third, we review and discuss current approaches for privacy preservation. Finally, drawing on lessons learned from AAL, we provide insights on the challenges and opportunities that lie ahead. Part of our methodology is a statistical analysis performed on the IEEE publications database. We illustrate our work with AAL scenarios elaborated in cooperation with the city of Luxembourg.

Smart environment applications can be based on a large variety of different sensors that may support the same use case, but have specific advantages or disadvantages. Benchmarking can allow determining the most suitable sensor systems for a given application by calculating a single benchmarking score, based on weighted evaluation of features that are relevant in smart environments. This set of features has to represent the complexity of applications in smart environments. In this work we present a benchmarking model that can calculate a benchmarking score, based on nine selected features that cover aspects of performance, the environment and the pervasiveness of the application. Extensions are presented that normalize the benchmarking score if required and compensate central tendency bias, if necessary. We outline how this model is applied to capacitive proximity sensors that measure properties of conductive objects over a distance. The model is used to identify existing and find potential new application domains for this upcoming technology in smart environments.

Previous studies have indicated the relation between a person’s gait related parameters and their health. Therefore, the ability to continuously monitor a person’s gait characteristics would be an advantage for caregivers. This paper proposes a solution that is able to estimate footstep locations based on audio measurements in a wireless acoustic sensor network (WASN). In realistic noisy environment this can however be difficult. A system proposed in previous work is first described and it is then discussed that it has difficulties to handle noisy environments. This paper proposes different modifications in order to improve noise robustness, i.e. average subtraction, multichannel Wiener filter and a noise robust footstep detector. These modifications and the original system are tested on a simulated dataset using stationary noise. This shows that an error reduction of 70% compared to the original system can be achieved. This improvement was confirmed on a real life dataset (error reduction of 60%). Finally the limits of the system are tested under highly non-stationary noise conditions. One modification was able to handle that difficult scenario under all SNR conditions (at best an error reduction of about 33% is observed in these experiments).

Activity classification consists in detecting and classifying a sequence of activities, choosing from a limited set of known activities, by observing the outputs generated by (typically) inertial sensor devices placed over the body of a user. To this end, machine learning techniques can be effectively used to detect meaningful patterns from data without explicitly defining classification rules. In this paper, we present a novel Body Sensor Network (BSN)-based low complexity activity classification algorithm, which can effectively detect activities performed by the user just analyzing the accelerometric signals generated by the BSN. A preliminary (computationally intensive) training phase, performed once, is run to automatically optimize the key parameters of the algorithm used in the following (computationally light) online phase for activity classification. In particular, during the training phase, optimized subsets of nodes are selected in order to minimize the number of relevant features and keep a good compromise between performance and time complexity. Our results show that the proposed algorithm outperforms other known activity classification algorithms, especially when using a limited number of nodes, and lends itself to real-time implementation.

Context-aware applications are intended to facilitate the adaptation of services in a pervasive computing system. The semantic similarity between contexts and the application of a semantic similarity measure as a mechanism for service adaptation are topics that have yet to be thoroughly explored in the literature. This study measured semantic similarities between quantitative contextual and categorical variables in the field of pervasive computing. The measure was applied to a current context and to several reference contexts, which were predefined based on a contextual data set. Built on the overlap measure because of its simplicity, the proposed weighted method is easy to implement and can be used to evaluate the actual weight of each contextual variable.

Individuals’ preferences are an important consideration in society, and different personality traits may contribute to those preferences. Personality traits can be used to understand what people would prefer or like about certain events or activities. Despite this, it appears that there is little understanding about the role of personality characteristics in visual design display. This study investigated the role of personality traits in users’ preferences. We examined the eye-movement behavior of 50 participants to identify their preferences in visual design presentations. A Bagging classifier with a genetic search method was used to assess the predictions of eye parameters based on personality dimensions. The results showed that high conscientiousness and agreeableness tended to influence eye-movement behavior toward visual design. Our findings may offer new insights for human-computer interaction, personalization, and rational choice theories. This study also addresses new trends related to the regulation of eye movements toward preferred visual design elements based on personality traits.