Ubiquitous Computing and Ambient Intelligence—UCAmI

The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991 [1] has recently evolved to a more general paradigm known as Ambient Intelligence (AmI) that represents a new generation of user-centred computing environments and systems. These solutions aim to find new ways to better integrate information technology into everyday life devices and activities. AmI environments are integrated by several autonomous computational devices of modern life ranging from consumer electronics to mobile phones. Ideally, people in an AmI environment will not notice these devices, but will benefit from the services these solutions provide them. Such devices are aware of the people present in those environments by reacting to their gestures, actions, and context [2]. Recently the interest in AmI environments has grown considerably due to new challenges posed by society’s demand for highly innovative services, such as smart environments, Ambient Assisted Living (AAL), e-Health, Internet of Things, and intelligent systems, among others.The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991 [1] has recently evolved to a more general paradigm known as Ambient Intelligence (AmI) that represents a new generation of user-centred computing environments and systems. These solutions aim to find new ways to better integrate information technology into everyday life devices and activities. AmI environments are integrated by several autonomous computational devices of modern life ranging from consumer electronics to mobile phones. Ideally, people in an AmI environment will not notice these devices, but will benefit from the services these solutions provide them. Such devices are aware of the people present in those environments by reacting to their gestures, actions, and context [2]. Recently the interest in AmI environments has grown considerably due to new challenges posed by society’s demand for highly innovative services, such as smart environments, Ambient Assisted Living (AAL), e-Health, Internet of Things, and intelligent systems, among others

Review of Ultra Wide Band (UWB) for Indoor Positioning with application to the elderly

The objective of this review is to analyze Ultra Wide Band (UWB) technology, as an option that allows developing new solutions in indoor positioning systems (IPS), mainly with a approach applied to the elderly. The methodology that has been applied corresponds to the definition of some basics concepts about UWB and some tests in the lab; the above to demonstrate the degree of accuracy that UWB offers compared to other technologies. The findings found and presented in this paper correspond to the identification of UWB as a technology with a high degree of accuracy for IPS; also, that there are other works related to the subject, with application in different areas, but specifically as an application for older people; regarding to the tests, these allowed to verify in the laboratory the operation and accuracy of UWB, for its possible application in IPS. The research described in this paper is the beginning of a implementation in a residence center, where accuracy in location and real-time response are important, in the future we hope make conclusive contributions of the implementations made

Indoor localisation through object detection within multiple environments utilising a single wearable camera

The recent growth in the wearable sensor market has stimulated new opportunities within the domain of Ambient Assisted Living, providing unique methods of collecting occupant information. This approach leverages contemporary wearable technology, Google Glass, to facilitate a unique first-person view of the occupants immediate environment. Machine vision techniques are employed to determine an occupant’s location via environmental object detection. This method provides additional secondary benefits such as first person tracking within the environment and lack of required sensor interaction to determine occupant location. Object recognition is performed using the Oriented Features from Accelerated Segment Test and Rotated Binary Robust Independent Elementary Features algorithm with a K-Nearest Neighbour matcher to match the saved key-points of the objects to the scene. To validate the approach, an experimental set-up consisting of three ADL routines, each containing at least ten activities, ranging from drinking water to making a meal were considered. Ground truth was obtained from manually annotated video data and the approach was previously benchmarked against a common method of indoor localisation that employs dense sensor placement in order to validate the approach resulting in a recall, precision, and F-measure of 0.82, 0.96, and 0.88 respectively. This paper will go on to assess to the viability of applying the solution to differing environments, both in terms of performance and along with a qualitative analysis on the practical aspects of installing such a system within differing environments

Sistema de Recomendación de Canciones OL-RadioUJA. Ampliación de Funcionalidades

La radio por Internet es un servicio con gran atractivo para los internautas en general, en cuyo ámbito destaca la reciente irrupción de radios personalizadas colaborativas, que ayudan al usuario a encontrar nueva música de su agrado, basándose en las preferencias de la música que ya ha escuchado el usuario. En 2009 fue presentada OL-RadioUJA, una radio personalizada sobre la que subyace un sistema de recomendación con filtrado colaborativo de canciones, las cuales se encuentran bajo algún tipo de licencia Creative Commons. La principal limitación de OL-RadioUJA es el reducido conjunto de canciones disponible, por lo que los usuarios, cansados de escuchar siempre las mismas canciones, dejaban de utilizar dicho servicio. En este artículo presentamos una nueva versión de OL-RadioUJA que contiene un nuevo módulo para incrementar la base de datos de canciones de la radio y así solventar la carencia del número de canciones. Para ello, dicho módulo permite que cualquier grupo musical pueda incorporar nuevas obras musicales (canciones) a OL-RadioUJA y gestionar el tipo licencia deseada para sus obras. Así, nuevas canciones son incorporadas a la radio colaborativa con la posibilidad de ser recomendadas a los usuarios. La inclusión de nuevas canciones origina el principal problema de los sistemas de recomendación con filtrado colaborativo: arranque frío o cold-start. En este artículo, presentaremos también una solución a dicho problema, la cual está basada en el género musical preferente de usuarios de OL-RadioUJA. Finalmente, nuevas funcionalidades relacionadas con las redes sociales que han sido incorporadas a la última versión de la radio serán presentadas

a mobile application for easy design and testing of algorithms to monitor physical activity in the workplace

This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available on-board accelerometer sensor. In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like in modern workplace environments. Increased sitting time is significantly associated with severe health diseases, and the workplace is an appropriate intervention setting, due to the sedentary behavior typical of modern jobs. Within this paper, the state-of-the-art components of HAR are analyzed, in order to identify and select the most effective signal filtering and windowing solutions for physical activity monitoring. The classifier development process is based upon three phases; a feature extraction phase, a feature selection phase, and a training phase. In the training phase, a publicly available dataset is used to test among different classifier types and learning methods. A user-friendly Android-based smartphone application with low computational requirements has been developed to run field tests, which allows to easily change the classifier under test, and to collect new datasets ready for use with machine learning APIs. The newly created datasets may include additional information, like the smartphone position, its orientation, and the user's physical characteristics. Using the mobile tool, a classifier based on a decision tree is finally set up and enriched with the introduction of some robustness improvements. The developed approach is capable of classifying six activities, and to distinguish between not active (sitting) and active states, with an accuracy near to 99%. The mobile tool, which is going to be further extended and enriched, will allow for rapid and easy benchmarking of new algorithms based on previously generated data, and on future collected datasets

A linguistic multigranular sensory evaluation model for olive oil

Evaluation is a process that analyzes elements in order to achieve different objectives such as quality inspection, marketing and other fields in industrial companies. This paper focuses on sensory evaluation where the evaluated items are assessed by a panel of experts according to the knowledge acquired via human senses. In these evaluation processes the information provided by the experts implies uncertainty, vagueness and imprecision. The use of the Fuzzy Linguistic Approach 32 has provided successful results modelling such a type of information. In sensory evaluation it may happen that the panel of experts have more or less degree knowledge of about the evaluated items or indicators. So, it seems suitable that each expert could express their preferences in different linguistic term sets based on their own knowledge. In this paper, we present a sensory evaluation model that manages multigranular linguistic evaluation framework based on a decision analysis scheme. This model will be applied to the sensory evaluation process of Olive Oil

Real-time Recognition of Interleaved Activities based on Ensemble of LSTM and Fuzzy Temporal Windows

In this paper, we present a methodology for Real-Time Activity Recognition of Interleaved Activities based on Fuzzy Logic and Recurrent Neural Networks. Firstly, we propose a representation of binary-sensor activations based on multiple Fuzzy Temporal Windows. Secondly, an ensemble of activity-based classifiers for balanced training and selection of relevant sensors is proposed. Each classifier is configured as a Long Short-Term Memory with self-reliant detection of interleaved activities. The proposed approach was evaluated using well-known interleaved binary-sensor datasets comprised of activities of daily living

Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results

Experiencias de tutorización en el Grado de Ingeniería Informática

La implantación del EEES está originando cambios en el modelo docente, cada vez más orientado al trabajo autónomo del alumno. En este nuevo paradigma, la figura del profesor también cambia para adquirir nuevos roles. Así, los profesores y los alumnos de la actual enseñanza universitaria buscan metodologías y herramientas que hagan posible alcanzar los nuevos retos que se plantean. Dentro de las posibles metodologías y herramientas para alcanzar dicho objetivos se encuentra la acción tutorial que es una herramienta que busca la interrelación activa con el alumno/a, realizando una orientación personalizada a través de los tutores. Generalmente, la acción tutorial es lanzada desde cada universidad para que los diferentes centros la pongan en marcha de acuerdo al contexto de las titulaciones de grado que imparte. El objetivo de este trabajo es presentar y comparar la acción tutorial que se está llevando a cabo desde dos centros pertenecientes a la Universidad Politécnica de Valencia y a la Escuela Politécnica Superior de Jaén con el objetivo de analizar ambos planes de acción tutorial y extraer conclusiones basadas en ambas experiencias.SUMMARY -- The implementation of the European Higher Education Area (EHEA) involves changes in the teaching model, increasingly the self-learning. In this new paradigm, the teacher also changes to acquire new roles. Therefore, teachers and students of the current universities search methodologies and tools that provide to meet new challenges. Among methodologies and tools to achieve these goals, we can find the tutorial action plan which is a tool that provides a relationship with the student, offering a personalized guidance by a teacher-tutor. Generally, the tutorial is launched from each university for its different centers. The aim of this contribution is to present and compare the tutorial action plan in two different Spanish universities, Universidad Politécnica de Valencia and Escuela Politécnica Superior de Jaén, in order to analyze both plans and obtain some conclusions about the experience