Abstract
People facing threats of mobility loss have their self-confidence shaken and tend to reduce their physical activity. As is well-known, the decreased physical activity, particularly for the elderly, is one of the factors that contribute to accelerating the deterioration of their health with consequent loss of autonomy and quality of life. Today, GPS-based technologies available on mobile devices offer many solutions to help guide users around much of the world. However, there are several known factors that act as barriers to the use of these technologies, such as user unfamiliarity with these devices, the complexity of geographical information and the difficulty of typing the origin and destination locations. In this paper we propose a solution for mobile devices that seeks to promote user confidence in daily mobility, especially among the elderly. We present the main system functionalities and the interface design. © 2014 Springer International Publishing.

Abstract
Any form of eating disorder is detrimental for health. Having an eating disorder increases the risks for chronic diseases and general morbidity, leading to several health problems such as obesity, hypertension and cardio-vascular diseases. The risk is greater for elderly people, as ageing submits the body to several functional changes that affect health and nutrition conditions. Automatic monitoring systems can help to prevent these risks by supporting people to maintain appropriate eating behaviours. Ageing services based on ICT assistive services are increasing as a result of the awareness of the growing socio-economic relevance of this issue, especially when we consider the rural and very sparsely-populated areas. In order to assess these requirements, systems should be automatic, non-intrusive and low cost. This paper presents an evaluation test of the Microsoft Kinect sensor for monitoring older people's meal intake, with the aim of contributing to the development of an automatic diet monitoring system. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Abstract
The use of Augmented Reality (AR) techniques to visualize virtual archaeological sites is neither a new or recent issue. In those approaches the virtual models are only visualized using the existent in situ illumination, which does not allow a visitor to have a similar visual experience to that which he would have at the time the structures were built. In Augmented Virtuality (AV) approaches the virtual world prevails, which is augmented with information from the real world, which allows a better control over the parameters of the Mixed Reality (MR) environment created. In some cases, there is the need to use both approaches (AR or AV), depending on some context conditions. This paper proposes an architecture and an information system for an adaptive MR system which main goal is to visualize in situ virtual reconstructions of archaeological sites that are seamlessly merged with the real scene. In this context, a new adaptive methodology will be defined to manage the level of mixing between the real and the virtual scene, identifying in each instant the most proper approach to use (AR or AV), as well as defining the way how transitions between approaches are made. (C) 2014 The Authors. Published by Elsevier Ltd.

Abstract
The quantification of low rate active tectonic structures is a major target of geodetic and geological studies to improve the knowledge of seismic hazards. The central Betic Cordillera (southern Spain) is affected by moderately active tectonic structures and seismicity. Part of this seismic activity is produced by several NW-SE normal faults located in the E of the Granada Basin. Here, we apply Multi-temporal InSAR (MTI) data to quantify the deformation produced by the Granada fault and the Padul fault zones and the surrounding area. The Granada NW-SE active normal fault zone, 17 km in length, crosses the city of Granada, a very sensitive area from a seismic hazard point of view due to the population of the Granada town. At the Padul fault, there is no geodetic evidence of contemporary motion. Considering the evidence of recent activity from geologic data, this fault may experience discontinuous motion with a different seismogenic character. Despite the InSAR uncertainties, InSAR results are consistent with the estimated geologic deformation rates lower than 1 mm/yr. Our results also confirm previous InSAR studies in the Otura area showing an estimated average annual velocity along the SAR line-of-sight of up to 10 mm/year anthropogenic subsidence. (C) 2014 The Authors. Published by Elsevier Ltd.

Abstract

Abstract
Three-dimensional object detection and recognition is increasingly in manipulation and navigation applications in autonomous service robots. It involves clustering points of the point cloud from an unstructured scene into objects candidates and estimating features to recognize the objects under different circumstances such as occlusions and clutter. This paper presents an efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner. In this paper, "open-ended" implies that the set of object categories to be learned is not known in advance. The training instances are extracted from actual experiences of a robot, and thus become gradually available, rather than being available at the beginning of the learning process. This paper focuses on two state-of-the-art questions: (1) How to automatically detect, conceptualize and recognize objects in 3D unstructured scenes in an open-ended manner? (2) How to acquire and utilize high-level knowledge obtained from the user (e. g. category label) to improve the system performance? This approach starts with a pre-processing phase to remove unnecessary information and prepare a suitable point cloud. Clustering is then applied to detect object candidates. Subsequently, all object candidates are described based on a 3D shape descriptor called spin-image. Finally, a nearest-neighbor classification rule is used to assign category labels to the detected objects. To examine the performance of the proposed approach, a leave-one-out cross validation algorithm is utilized to compute precision and recall. The experimental results show the fulfilling performance of this approach on different types of objects.