Indoor positioning technologies for academic libraries: towards the smart library

Se presenta un caso de uso de la aplicación de tecnologías geoespaciales en una biblioteca universitaria. En concreto, se han desarrollado dos aplicaciones capaces de obtener la localización de los ejemplares buscados por los usuarios, una para dispositivos móviles con sistema operativo Android y la otra para su uso en navegadores web. Dichas aplicaciones permiten dibujar la ruta desde el punto en el que el usuario se sitúa hasta la estantería donde se encuentran los ejemplares, mejorando así la experiencia del usuario. Adicionalmente, el artículo investiga las posibilidades de aplicación de sistemas de información geográfica (SIG) y tecnologías geoespaciales para la mejora de los servicios ofrecidos por la biblioteca.This article is a case study of the application of geospatial technologies in an academic library. More specifically, two applications were developed to assist users in finding the location of books, one for Android mobile devices and the other for web browsers. These applications provide users with the ability to plot the path from the point where the user is located to the shelf where the items are found, thereby improving the user experience. Additionally, this article investigates the possibility of applying geographic information systems (GIS) and geospatial technologies to library services

TAG: A Tabletop Games Framework

Esta ponencia forma parte de : 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020)Tabletop games come in a variety of forms, including board games, card games, and dice games. In recent years, their complexity has considerably increased, with many components, rules that change dynamically through the game, diverse player roles, and a series of control parameters that influence a game’s balance. As such, they also encompass novel and intricate challenges for Artificial Intelligence methods, yet research largely focuses on classical board games such as chess and Go. We introduce in this work the Tabletop Games (TAG) framework, which promotes research into general AI in modern tabletop games, facilitating the implementation of new games and AI players, while providing analytics to capture the complexities of the challenges proposed. We include preliminary results with sample AI players, showing some moderate success, with plenty of room for improvement, and discuss further developments and new research direction

A study of deep neural networks for human activity recognition

Human activity recognition and deep learning are two fields that have attracted attention in recent years. The former due to its relevance in many application domains, such as ambient assisted living or health monitoring, and the latter for its recent and excellent performance achievements in different domains of application such as image and speech recognition. In this article, an extensive analysis among the most suited deep learning architectures for activity recognition is conducted to compare its performance in terms of accuracy, speed, and memory requirements. In particular, convolutional neural networks (CNN), long short‐term memory networks (LSTM), bidirectional LSTM (biLSTM), gated recurrent unit networks (GRU), and deep belief networks (DBN) have been tested on a total of 10 publicly available datasets, with different sensors, sets of activities, and sampling rates. All tests have been designed under a multimodal approach to take advantage of synchronized raw sensor' signals. Results show that CNNs are efficient at capturing local temporal dependencies of activity signals, as well as at identifying correlations among sensors. Their performance in activity classification is comparable with, and in most cases better than, the performance of recurrent models. Their faster response and lower memory footprint make them the architecture of choice for wearable and IoT devices

Magnetic field based Indoor positioning using the Bag of Words paradigm

Ponencia presentada en 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 4-7 Oct. 2016In this paper, A Bag of Words based method is presented to test a magnetic field based indoor positioning method. The Indoor positioning problem is solved as a pattern recognition problem, where each reference point is a different class. Feature vectors are constructed using a simplified bag of words methodology allowing user speed invariance. Several well known classifiers have been used to test the proposed method obtaining promising results when recognition the position of the user

Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

This paper presents our experience on a real case of applying an indoor localization system formonitoringolderadultsintheirownhomes. Sincethesystemisdesignedtobeusedbyrealusers, therearemanysituationsthatcannotbecontrolledbysystemdevelopersandcanbeasourceoferrors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task

Magnetic Field as a Characterization of Wide and Narrow Spaces in a Real Challenging Scenario Using Dynamic Time Warping

Ponencia presentada en 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 24-27 Sept. 2018This paper presents a study of indoor positioning in public zones of the Parc Taulí Hospital in Sabadell. It is a challenging scenario because: (1) it combines wide spaces with middle sized and narrow spaces; (2) it is a shielded zone where no signals are available, and therefore, no WiFi signal can be used for positioning; and (3) it is not possible to deploy beacons for positioning. The goal of this work is to test whether it is possible to get indoor positioning in a real and challenging scenario by using only the magnetic field. The positioning precision requires to locate the part of the hospital where the user is. The proposed solution defines “virtual corridors” to improve positioning in wide areas. To validate the work, magnetic field data have been recorded from the scenario, using different smartphones and by different persons. The obtained magnetic data curves have been compared by using dynamic time warping distance. Results show that it is possible to characterize every path with the magnetic field. The main contributions of the present paper are: (1) defining “virtual corridors” as a way to position using magnetic field in 2D spaces; and (2) showing that even in wide spaces, like the hall of a hospital, it is possible to find magnetic anomalies linked to positions

Indoor Positioning and Fingerprinting:The R Package ipft

Methods based on Received Signal Strength Indicator (RSSI) fingerprinting are in theforefront among several techniques being proposed for indoor positioning. This paper introducesthe R packageipft, which provides algorithms and utility functions for indoor positioning usingfingerprinting techniques. These functions are designed for manipulation of RSSI fingerprint datasets, estimation of positions, comparison of the performance of different positioning models, andgraphical visualization of data. Well-known machine learning algorithms are implemented in thispackage to perform analysis and estimations over RSSI data sets. The paper provides a descriptionof these algorithms and functions, as well as examples of its use with real data. Theipftpackageprovides a base that we hope to grow into a comprehensive library of fingerprinting-based indoorpositioning methodologies

Equivalence of chatbot and paper-and-pencil versions of the De Jong Gierveld loneliness scale

This is a post-peer-review, pre-copyedit version of an article published in Current Psychology. The final authenticated version is available online at: https://doi.org/10.1007/s12144-020-01117-0Technological progress provides health professionals with an excellent opportunity to take advantage of these developments and contribute to the development of efficient ways of diagnosing, monitoring, treating and assisting users. The purpose of this work is to present the results of a study conducted to examine the quantitative equivalence of paper-and-pencil and a voice-based conversational assistant, popularly known as a “chatbot”, as means to administer tests. One hundred and eight undergraduate university students completed both versions of the De Jong Gierveld Loneliness Scale. The interval between the first and second administration was set at four days. Validity, internal structure, internal consistency and equivalence of chatbot administration mode were assessed. A confirmatory factor analysis was used to verify the factor structure and provided a two-factor structure. Validity and internal consistency are adequate. These results support the feasibility of using chatbots for loneliness assessment in a sample of undergraduate university students and other populations in future

BLE-GSpeed: A new BLE- based dataset to estimate user gait speed

To estimate the user gait speed can be crucial in many topics, such as health care systems, since the presence of difficulties in walking is a core indicator of health and function in aging and disease. Methods for non-invasive and continuous assessment of the gait speed may be key to enable early detection of cognitive diseases such as dementia or Alzheimer’s disease. Wearable technologies can provide innovative solutions for healthcare problems. Bluetooth Low Energy (BLE) technology is excellent for wearables because it is very energy efficient, secure, and inexpensive. In this paper, the BLE-GSpeed database is presented. The dataset is composed of several BLE RSSI measurements obtained while users were walking at a constant speed along a corridor. Moreover, a set of experiments using a baseline algorithm to estimate the gait speed are also presented to provide baseline results to the research community

In-home monitoring system based on WiFi fingerprints for ambient assisted living

This paper presents an in-home monitoring system based on WiFi fingerprints for Ambient Assisted Living. WiFi fingerprints are used to continuously locate a patient at the different rooms in her/his home. The experiments performed provide a correctly location rate of 96% in the best case of all studied scenarios. The behavior obtained by location monitoring allows to detect anomalous behavior such as long stays in rooms out of the common schedule. The main characteristics of the presented system are: a) it is robust enough to work without an own WiFi access point, which in turn means a very affordable solution; b) low obtrusiveness, as it is based on the use of a mobile phone; c) highly interoperable with other wireless connections (bluetooth, RFID) present in current mobile phones; d) alarms are triggered when any anomalous behavior is detected