Catálogo de publicaciones - libros

With the increasing number of scientific publications, the analysis of the trends and the state-of-the-art in a certain scientific field is becoming very time-consuming and tedious task. In response to urgent needs of information, for which the existing systematic review model does not well, several other review types have emerged, namely the rapid review and scoping reviews. In this paper, we propose an NLP powered tool that automates most of the review process by automatic analysis of articles indexed in the IEEE Xplore, PubMed, and Springer digital libraries. We demonstrate the applicability of the toolkit by analyzing articles related to Enhanced Living Environments and Ambient Assisted Living, in accordance with the PRISMA surveying methodology. The relevant articles were processed by the NLP toolkit to identify articles that contain up to 20 properties clustered into 4 logical groups. The analysis showed increasing attention from the scientific communities towards Enhanced and Assisted living environments over the last 10 years and showed several trends in the specific research topics that fall into this scope. The case study demonstrates that the NLP toolkit can ease and speed up the review process and show valuable insights from the surveyed articles even without manually reading of most of the articles. Moreover, it pinpoints the most relevant articles which contain more properties and therefore, significantly reduces the manual work, while also generating informative tables, charts and graphs.

Handling large knowledge bases of information from different domains such as the World Wide Web is a complex problem addressed in the Resource Description Framework (RDF) by adding semantic meaning to the data itself. The amount of linked data has brought with it a number of specialized databases that are capable of storing and processing RDF data, called RDF stores. We explore the RDF store landscape with the aim of finding an RDF store that sufficiently meets the storage needs of an enhanced living environment, more concretely the requirements of a Smart Space platform aimed at running on a cluster set up of low-power hardware that can be run locally entirely at home with the purpose of logging data for a reactive assistive system involving, e.g., activity recognition or domotics. We present a literature analysis of RDF stores and identify promising candidates for implementation of consumer Smart Spaces. Based on the insights provided with our study, we conclude by suggesting different relevant aspects of RDF storage systems that need to be considered in Ambient Assisted Living environments and a comparison of available solutions.

The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems.

In this paper, a personal recommendation system of outdoor physical activities using solely user’s history data and without application of collaborative filtering algorithms is proposed and evaluated. The methodology proposed contains four phases: data fuzzification, activity usefulness calculation, estimation of most useful activities, activities classification. In the process of classification several data mining techniques were compared such as: decision trees algorithms, decision rules algorithm, Bayes algorithm and support vector machines. The proposed algorithm has been experimentally validated using real dataset collected in a certain period of time from a community of 1000 active users. Recommendations generated by the system were related to weight loss. The results show that our generated recommendations have high accuracy, up to 95%.

Frailty is a common clinical syndrome in older adults; it carries an increased risk of negative health events and outcomes including falls, incident disability, hospitalization, and mortality. Therefore, it is critical to identify high-risk subsets of the elderly population and explore new arenas for frailty prevention and treatment. This chapter will provide an overview of the current state of assessment models for frailty syndrome in the elderly and will describe a new assessment tool based on mobile technology, which takes account and advantage of the ways in which elderly people interact with a touchscreen. While healthcare providers and researchers in the field of aging have long been aware of the changing characteristics and needs of older people living in the community, there has not been any marked change in frailty syndrome assessment models until now. In the twenty-first century world with its technological advancements, the elderly require new, special physical skills combining perceptual, motor, and cognitive abilities for their functional daily activities and for maintaining their independence and quality of life. We believe a conceptual model of frailty can be expanded to incorporate new aspects related to the usage of technology by the elderly, better covering the complexity and multidimensionality of modern life. In addition, in our vision, that the expanded conceptual model can be operationalized and translated into an assessment tool. In the last part of this chapter, we will present the “Touchscreen Assessment Tool” (TATOO), an assessment tool based on this expanded conceptual model. This novel tool assesses elderly people’s frailty and functioning using a touchscreen, a representative technology required for several activities involved in daily functioning in the modern world. Most importantly, we present the TATOO prototype, which we plan to develop in the future as a continuous monitoring instrument for activities performed in daily life, combined with advanced sensor-based measuring and big data analytics algorithms.

In this chapter, we investigate the extent to which the real-time bidirectional exchange of activity information can influence context-awareness, social presence, social connectedness, and importantly interpersonal activity synchrony in mediated ambient assisted living (AAL) environments. Additionally, we describe the design, development, and assessment of a bidirectional ambient display platform to support real-time activity awareness and social connectedness in mediated AAL contexts. In a semi-controlled study, we evaluate a conglomerate of activity-based lighting displays, to determine the effects of real-time bidirectional deployment on behaviour and social connectedness. Exploiting everyday objects, human activity levels are projected with a Philips Hue lamp, LED wallet, and LED walking cane, which render this information based on predefined patterns of light. Results from the current study show tendencies toward (1) an increase in implicit social interactions ( e.g., the sense of experienced social presence and connectedness), (2) more positive social behaviours between the elderly and their caregivers in mediated AAL contexts, and (3) sporadic moments of interpersonal activity synchrony however, further investigation is necessary to determine the extent of this variable in mediated AAL contexts.

Affective computing is a growing field of artificial intelligence. It focuses on models and strategies for detecting, obtaining, and expressing various affective states, including emotions, moods, and personality related attributes. The techniques and models developed in affective computing are applicable to various affective contexts, including Ambient Assisted Living. One of the hypotheses for the origin of emotion is that the primary purpose was to regulate social interactions. Since one of the crucial characteristics of Ambient Assisted Living systems is supporting social contact, it is unthinkable to build such systems without considering emotions. Moreover, the emotional capacity needed for Ambient Assisted Living systems exceeds simple user emotion detection and showing emotion expressions of the system. In addition, emotion generation and emotion mapping on rational thinking and behavior of a system should be considered. The chapter discusses the need and requirements for these processes in the context of various application domains of Ambient Assisted Living, i.e., healthcare, mobility, education, and social interaction.

We describe herein the problem of providing the most cost efficient and effective ways of supporting mental wellbeing as well as methods for physical and mental rehabilitation for elderly at home including a recovery from accidents, particularly concentrating on those impacting brain activities, such as aging-related dementia and stroke, illnesses with very high socio-economic influence. Technologies built in several EU funded projects, e.g. FP7-ICT-StrokeBack, FP7-ICT-ARMOR, Artemis-CHIRON, FP7-SEC-AF3 and other ones, are suitable also for other kinds of health issues, such as recovery from injuries, restoring mobility etc. A common part is stimulating engagement through entertainment, rivalry and “real feeling” of gaming environment, motivating compliance with rehabilitation rules. The automated home system combining progress in ICT and applied clinical know-how allows patients, their direct care providers and family to back the effective use of rehabilitation procedures in their familiar home surroundings instead of unfriendly clinical settings. Our system integrates a set of state of art technologies ranging from augmented/virtual reality gaming, merged with immersive user interfaces for providing mixed reality exercise setting, innovative embedded micro sensor devices with improved power autonomy through use of the newest Bluetooth Smart communication transceivers, combined together into a Personal Health Record (PHR) system supporting the delivery of individual, patient-centred e-health services both at home, at hospital or when mobile. The use of mixed-reality systems, merging interactive virtual components with realistic settings of patient’s home, is linked with multi-modal user interfaces stimulating operation of his/her body to accomplish the objective of the exercise, while self-motivation is inspired by rivalry with oneself and other people. This gears to achieving better individual’s contribution to rehabilitation procedure, leading to attaining meaningfully quicker regaining of one’s earlier abilities. The physiological data is combined with and related to the detected body motion sensing using novel feature extraction and classification procedures handled within a wearable unit, to determine the precision of performed workouts. By using physical intervention only when essential, this disregards expensive human involvement and thus significantly decreases related expenses of Public Health Care services. We start with describing the motivation and needs for such system in Sect.  2 , which gives raise to deriving system specifications and architecture as described in Sect.  3 . In following Sects.  4 and 5 we described specific technologies developed in our projects, namely Mixed-Reality Rehabilitation Training System integrated into a Personal Health Record (PHR) platform. We then provide the overview of the overall system integrated into a portable unit in Sect.  6 , concluding with report on evaluations with users in Sect.  7 .

Nowadays, sensors, algorithms and software applications are embedded in people’s everyday lives. To be more precise, living environment is significantly dependent on previously mentioned technology especially in the field of medicine. The ultimate goal of this research is to collect data from different medical sensors platform, which can be used in initial medical assessment and management in the living environment. Furthermore, collected data from sport’s activity were analysed in order to measure the strength of linear association between variables. First, research on market available equipment is performed which is focused on medical sensors for assisted living in people’s everyday environment. Second, research on hardware in terms of tablet, desktop machines, iPad etc. is conducted and scenarios of using the hardware are elaborated. Regarding the scenarios, the concept that demonstrates the advantages and disadvantages of the system for ambient assisted living is shown. The architecture of the system followed by sensors, computer networks, terminals is included and the technology for achieving the system’s functionality is presented. Next, the Zephyr Bioharness 3 sensor for measuring physiological data is used for collecting data in the living environment. Finally, integration of the sensor with the most convenient terminal in the living environment is done and collected data are visualized via MATLAB. The parameters monitored are Heart Rate (HR), Respiratory Rate (RR), Peak Acceleration (PkAcc). The proof of the correlation between listed parameters describes the physiological status in the living environment. The ratio between HR and PkAcc is 0.91. It is a strong positive correlation. It means that changes in heart beats are linearly followed by the peak acceleration. Next, the ratio between HR and RR is 0.88. It is also a strong positive correlation. It means that changes in heart beats are linearly followed by the respiratory rate. The ratio between RR and PkAcc is 0.84. Again, it is a strong positive correlation. It means that changes in respiratory rate are linearly followed by the peak acceleration.

This chapter presents an overview of many wearable devices of different types that have been proven in medical and home environments as being helpful in Quality of Life enhancement of elder adults. The recent advances in electronics and microelectronics allow the development of low-cost devices that are widely used by many people as monitoring tools for well-being or preventive purposes. Remote healthcare monitoring, which is based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers efficient solutions that allows people to live in their comfortable home environment, being somehow protected. Furthermore, the expensive healthcare facilities are getting free to be used for intensive care patients as the preventive measures are getting at home. The remote systems can monitor very important physiological parameters of the patients in real time, observe health conditions, assessing them, and most important, provide feedback. Sensors are used in electronics medical and non-medical equipment and convert various forms of vital signs into electrical signals. Sensors can be used for life-supporting implants, preventive measures, long-term monitoring of disabled or ill patients. Healthcare organizations like insurance companies need real-time, reliable, and accurate diagnostic results provided by sensor systems that can be monitored remotely, whether the patient is in a hospital, clinic, or at home.