Abstract
Population aging brings increased social problems. Solutions for this new reality must be devised. Providing care services at home may benefit patients, health service providers, and social security systems and needs to be seen as a possible solution for those social problems. By maintaining the patient at home, in his or her own environment, care services costs can be diminished and, at the same time, the comfort and well-being of the person in need are significantly increased. To pursue this goal, we explore the advantages that ambient assisted living can bring to people in a home environment, focusing on the problems of health care services at home. Specifically, in this chapter, we present a framework focused on the monitoring and assistance of the elderly that are living alone, focusing on those elderly with memory disabilities. We believe that this approach will enable the challenges that the current trend of population aging poses to be tackled. © 2015 by Taylor & Francis Group, LLC.

Abstract
Progressively increasing complexity of dynamic environments, in which services and applications are demanded by potential clients, requires a high level of reconfiguration of the offer to better match that ever changing demand. In particular, the dynamic change of the client's needs, leading to higher exigency, may require a smart and flexible automatic composition of more elementary services. By leveraging the service-oriented architectures and multi-agent system benefits, the paper proposes a method to explore the flexibility of the decision support for the services' reconfiguration based on several pillars, such as trust, reputation and QoS models, which allows the selection based on measuring the expected performance of the agents. Preliminary experimental results, extracted from a real case scenario, allow highlighting the benefits of the proposed distributed and flexible solution to balance the workload of service providers in a simple and fast manner. The proposed solution includes the agents' intelligent decision-making capability to dynamically and autonomously change services selection on the fly, towards more trustworthy services with better quality when unexpected events happen, e.g. broken machines. We then propose the use of competitive self-interested agents to provide services that best suits to the client through dynamic service composition.

Abstract
Purpose - The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing. Design/methodology/approach - In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope. Findings - Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor. Research limitations/implications - The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed. Practical implications - Today, most of human-robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors. Originality/value - Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human-robot interaction scenario show the performance of the system.

Abstract
The optimal solution provided by metaheuristics can be viewed as a random variable, whose behavior depends on the value of the algorithm's strategic parameters and on the type of penalty function used to enforce the problem's soft constraints. This paper reports the use of parametric and non-parametric statistics to compare three different penalty functions implemented to solve the Security Constrained Optimal Power Flow (SCOPF) problem using the new enhanced metaheuristic Differential Evolutionary Particle Swarm Optimization (DEEPSO). To obtain the best performance for the three types of penalty functions, the strategic parameters of DEEPSO are optimized by using an iterative algorithm based on the two-way analysis of variance (ANOVA). The results show that the modeling of soft constraints significantly influences the best achievable performance of the optimization algorithm.

Abstract

Abstract
Although most current pointing devices rely on relative rotation increments, absolute orientation allows for a more intuitive interaction. However, this is difficult to implement in low-energy consumption devices since accurate fusion filters are computationally intensive. This work presents a comparative study of low-complexity filters and state-of-the-art orientation tracking systems, enabling to access complexity versus portability. A relevant set of different MARG units currently available on the market were studied under systematic tests and human subjective user analysis. Experimental results show that it is possible to obtain similar accuracy using low-complexity filters to the ones observed with state-of-the-art orientation tracking systems.