Even now, users with disabilities encounter serious barriers when accessing the Web. In particular, blind and visually impaired users encounter difficulties browsing and reading the contents of a website. Screen readers provide some assistance, yet, as they are unable to interpret the Web structure, they summarise information and read specific labelled fragments. Therefore, the overall comprehension of the text remains challenging. In this sense, in order to improve the accessibility of websites on the fly, we propose a Web augmentation framework for accessibility (WAFRA). Our framework uses Web augmentation techniques that extend the website with voice interaction and new actions: label text fragments, read aloud these fragments, facilitate navigation, increase font size and show videos. In order to perform this accessibility improvement, we automatically provide annotations from DBPedia regarding important information for end users. Moreover, we also provide the option that intermediary users add new annotations for labelling or including more specific information, which can be shared with other users by crowdsourcing. The evaluation of the framework shows its usefulness to ease website access for users with visual disabilities compared to using screen readers.

Improving the performance of Artificial Neural Network (ANN) regression models on small or scarce datasets, such as wireless network positioning data, can be realized by simplifying the task. One such approach includes implementing the regression model as a classifier, followed by a probabilistic mapping algorithm that transforms class probabilities into the multi-dimensional regression output. In this work, we propose the so-called c2r, a novel ANN-based architecture that transforms the classification model into a robust regressor, while enabling end-to-end training. The proposed solution can remove the impact of less likely classes from the probabilistic mapping by implementing a novel, trainable differential thresholded Rectified Linear Unit layer. The proposed solution is introduced and evaluated in the indoor positioning application domain, using 23 real-world, openly available positioning datasets. The proposed C2R model is shown to achieve significant improvements over the numerous benchmark methods in terms of positioning accuracy. Specifically, when averaged across the 23 datasets, the proposed c2r improves the mean positioning error by 7.9% compared to weighted knn with k=3, from 5.43 m to 5.00 m, and by 15.4% compared to a dense neural network (DNN), from 5.91 m to 5.00 m, while adapting the learned threshold. Finally, the proposed method adds only a single training parameter to the ann, thus as shown through analytical and empirical means in the article, there is no significant increase in the computational complexity.

The use of smartphone-based Serious Games in mental health care is an emerging and promising research field. Combining the intrinsic characteristics of games (e.g., interactiveness, immersiveness, playfulness, user-tailoring and engaging nature) with the capabilities of smartphones (e.g., versatility, ubiquitous connectivity, built-in sensors and anywhere-anytime nature) yields great potential to deliver innovative psychological treatments, which are engaging, effective, fun and always available. This article presents a scoping review, based on the PRISMA (scoping review extension) guidelines, of the field of smartphone-based serious games for mental health care. The review combines an analysis of the technical characteristics, including game design, smartphone and game-specific features, with psychological dimensions, including type and purpose of use, underlying psychological frameworks and strategies. It also explores the integration of psychological features into Serious Games and summarizes the findings of evaluations performed. A systematic search identified 40 smartphone-based Serious Games for mental health care. The majority consist of standalone and self-administrable interventions, applying a myriad of psychological strategies to address a wide range of psychological symptoms and disorders. The findings explore the potential of Serious Games as treatments and for enhancing patient engagement; we conclude by proposing several avenues for future research in order to identify best practices and success factors. Supplementary Information: The online version contains supplementary material available at 10.1007/s11042-024-18971-w.

Physical performance tests aim to assess the physical abilities and mobility skills of individuals for various healthcare purposes. They are often driven by experts and usually performed at their practice, and therefore they are resource-intensive and time-demanding. For tests based on objective measurements (e.g., duration, repetitions), technology can be used to automate them, allowing the patients to perform the test themselves, more frequently and anywhere, while alleviating the expert from supervising the test. The well-known Timed Up and Go (TUG) test, typically used for mobility assessment, is an ideal candidate for automation, as inertial sensors (among others) can be deployed to detect the various movements constituting the test without expert supervision. To move from expert-led testing to self-administered testing, we present a mHealth system capable of automating the TUG test using a pocket-sized smartphone or a wrist smartwatch paired with a smartphone, where data from inertial sensors are used to detect the activities carried out by the patient while performing the test and compute their results in real time. All processing (i.e., data processing, machine learning-based activity inference, results calculation) takes place on the smartphone. The use of both devices to automate the TUG test was evaluated (w.r.t. accuracy, reliability and battery consumption) and mutually compared, and set off with a reference method, obtaining excellent Bland-Altman agreement results and Intraclass Correlation Coefficient reliability. Results also suggest that the smartwatch-based system performs better than the smartphone-based system.

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.

Through the Internet of Things (IoT), autonomous sensing devices can be deployed to regularly capture environmental and other sensor measurements for a variety of usage scenarios. However, for the market segment of stand-alone, self-sustaining small IoT nodes, long-term deployment remains problematic due to the energy-constrained nature of these devices, requiring frequent maintenance. This article introduces Hypnos, an open hardware and software toolkit that aims to balance energy intake and usage through the adaptive sensing rate for low-cost Internet-connected IoT nodes. We describe the hardware architecture of the IoT node, an open hardware board based on the Arduino Uno form-factor packing the energy measurement circuitry, and the associated opensource software library, which interfaces with the sensing node’s microcontroller and provides access to the low-level energy measurements. Hypnos comes equipped with a built-in, configurable, modified sigmoid function to regulate duty-cycle frequency based on energy intake and usage, yet developers may also plug in their custom duty/sleep balancing function. An experiment was set up, whereby two identical boards ran for two months: one with the Hypnos software framework and built-in energy-balancing function to regulate sensing rate and the other with fixed sensing rate. The experiment showed that Hypnos is able to successfully balance energy usage and sensing frequency within configurable energy ranges. Hereby, it increases the reliability by avoiding the complete shutdown, while, at the same time, optimizing performance in terms of the average amount of sensor measurements.

In various usage scenarios, smartphones are used as measuring instruments to systematically and unobtrusively collect data measurements (e.g., sensor data, user activity, phone usage data). Unfortunately, in the race towards extending battery life and improving privacy, mobile phone manufacturers are gradually restricting developers in (frequently) scheduling background (sensing) tasks and impede the exact scheduling of their execution time (i.e., Android’s “best effort” approach). This evolution hampers successful deployment of smartphones in sensing applications in scientific contexts, with unreliable and incomplete sampling rates frequently reported in literature. In this article, we discuss the ins and outs of Android’s background tasks scheduling mechanism, and formulate guidelines for developers to successfully implement reliable task scheduling. Implementing these guidelines, we present a software library, agnostic from the underlying Android scheduling mechanisms and restrictions, that allows Android developers to reliably schedule tasks with a maximum sampling rate of one minute. Our evaluation demonstrates the use and versatility of our task scheduler, and experimentally confirms its reliability and acceptable energy usage.

Nowadays, the Bluetooth Low Energy (BLE) technology joined with the Received Signal Strength Indicator technique has became a popular approach in Indoor Positioning System, thanks to the wide availability of BLE in anchors and wearable devices and the straightforward implementation of both. Consequently, methods based on geometric properties of anchors, as lateration, are capable of enhancing the positioning accuracy exploiting the growing availability of anchors and their rich geometric distribution in indoor environments. On the downside, an inappropriate selection of anchors decreases the positioning accuracy estimation. Therefore, integrating an effective beacon selection method can enhance the reliability and accuracy of these methods. In this paper, we present a novel and straightforward Lateration indoor positioning method based on effective combinatorial BLE beacon selection. The combinatorial BLE selection approach relies on a geometrical analysis (difference of triangle areas), of each beacon combination, considering the reference beacons’ position with the estimated position using lateration, and with a globally calculated virtual target position as reference. The real-world experiment demonstrated that the proposed method improves the traditional lateration with 5% to 16%, considering different evaluation metrics.

Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.

A Smart City relies on six key factors: Smart Governance, Smart People, Smart Economy, Smart Environment, Smart Living and Smart Mobility. This paper focuses on Smart Mobility by improving one of its key components: positioning. We developed and deployed a novel indoor positioning system (IPS) that is combined with an outdoor positioning system to support seamless indoor and outdoor navigation and wayfinding. The positioning system is implemented as a service in our broader cartography-based smart university platform, called SmartUJI, which centralizes access to a diverse collection of campus information and provides basic and complex services for the Universitat Jaume I (Spain), which serves as surrogate of a small city. Using our IPS and based on the SmartUJI services, we developed, deployed and evaluated two end-user mobile applications: the SmartUJI APP that allows users to obtain map-based information about the different facilities of the campus, and the SmartUJI AR that allows users to interact with the campus through an augmented reality interface. Students, university staff and visitors who tested the applications reported their usefulness in locating university facilities and generally improving spatial orientation.

BACKGROUND. The term Rich Internet Applications (RIAs) is generally associated with Web applications that provide the features and functionality of traditional desktop applications. Ten years after the introduction of the term, an ample amount of research has been carried out to study various aspects of RIAs. It has thus become essential to summarize this research and provide an adequate overview. OBJECTIVE. The objective of our study is to assemble, classify, and analyze all RIA research performed in the scientific community, thus providing a consolidated overview thereof, and to identify well-established topics, trends, and open research issues. Additionally, we provide a qualitative discussion of the most interesting findings. This work therefore serves as a reference work for beginning and established RIA researchers alike, as well as for industrial actors that need an introduction in the field, or seek pointers to (a specific subset of) the state-of-the-art. METHOD. A systematic mapping study is performed in order to identify all RIA-related publications, define a classification scheme, and categorize, analyze, and discuss the identified research according to it. RESULTS. Our source identification phase resulted in 133 relevant, peer-reviewed publications, published between 2002 and 2011 in a wide variety of venues. They were subsequently classified according to four facets: development activity, research topic, contribution type, and research type. Pie, stacked bar, and bubble charts were used to depict and analyze the results. A deeper analysis is provided for the most interesting and/or remarkable results. CONCLUSION. Analysis of the results shows that, although the RIA term was coined in 2002, the first RIA-related research appeared in 2004. From 2007 there was a significant increase in research activity, peaking in 2009 and decreasing to pre-2009 levels afterwards. All development phases are covered in the identified research, with emphasis on “design” (33%) and “implementation” (29%). The majority of research proposes a “method” (44%), followed by “model” (22%), “methodology” (18%), and “tools” (16%); no publications in the category “metrics” were found. The preponderant research topic is “models, methods and methodologies” (23%) and, to a lesser extent, “usability and accessibility” and “user interface” (11% each). On the other hand, the topic “localization, internationalization and multilinguality” received no attention at all, and topics such as “deep Web” (under 1%), “business processing”, “usage analysis”, “data management”, “quality and metrics” (all under 2%), “semantics”, and “performance” (slightly above 2%) received very little attention. Finally, there is a large majority of “solution proposals” (66%), few “evaluation research” (14%), and even fewer “validation” (6%), although the latter have been increasing in recent years.

In a mobile setting, the user often browses the Web to consult information related to his current context and environment: e.g., reviews of nearby restaurants, or tourist information on visited monuments. On the other hand, the limitations of mobile devices (e.g., limited screen) and the peculiarities of mobile Web usage (e.g., walking around, driving a car) make it cumbersome to extensively browse a Web page for such useful information. In this paper, we present a client-side approach that aims to assist the mobile user in his browsing session, by correlating the Web page's content with the mobile user's context, and subsequently emphasizing and enriching relevant content with so-called context-sensitive cues. To achieve this, we utilize the SCOUT framework for mobile applications to model and access the user's context, and RDFa annotations present on existing Web pages to identify Web page elements suitable to enrich with context-sensitive cues. The cues themselves are injected using existing adaptation techniques, borrowed from the field of Adaptive Hypermedia.

Web-based systems can exploit Semantic Web-based approaches to link data and thus create applications that make the most out of the combination and integration of different sources and background knowledge. While a lot of attention is paid to the opportunities that this linking of data on the Web provides, the reality of implementing such solutions with currently available semantic technologies createsa serious engineering challenge. In developing such applications in a commercial setting, we have been confronted with requirements and conditions that show the limitations of current technologies for this type of Web applications. Using our experience from iFanzy, we illustrate in this paper the issues and steps in turning the concept of access to semantically integrated content into solutions that use available technology.

An important milestone in the evolution of the Web is the Semantic Web: a Web in which the semantics of the available content and functionality is made explicit. Web design methods, originally aimed at offering the designer a well-structured, systematic approach to Web design, now face new challenge: to facilitate the (semi-) automatic generation of semantic annotations describing both content and structure of the website, thereby enabling the Semantic Web. In this short paper, we describe how the existing design method WSDM was adapted to use Semantic Web technology and how the use of Semantic Web technology was exploited to support the generation of semantically annotated websites.

Web application design methods traditionally aim to reduce complexity in implementing Web applications. However, these methods struggle with providing the necessary dynamics and flexibility to keep up with the increasing users' demand for personalization and feedback mechanisms. We present Hera-S, based on Sesame and the SeRQL query language that does provide the necessary flexibility. Hera-S allows designers the plain use of the Semantic Web languages RDFS and OWL for designing the domain model and the context data model, thus enabling re-use of existing data models and opening up the RDF instance data to queries and updates via the Sesame RDF-framework.

The evolution of the Web requires to consider an increasing number of context-dependency issues. Therefore, in our research we focus on how to extend a Web application with additional adaptation concerns without having to redesign the entire application. Based on a generic transcoding tool we illustrate here how we can add adaptation functionality to an existing Web application. Furthermore, we consider how an aspect-oriented approach can support the high-level specification of such additional concerns in the design of the Web application.

Currently, the vast majority of web sites do not support accessibility for visually impaired users. Usually, these users have to rely on screen readers: applications that sequentially read the content of a web page in audio. Unfortunately, screen readers are not able to detect the meaning of the different page objects, and thus the implicit semantic knowledge conveyed in the presentation of the page is lost. One approach described in literature to tackle this problem, is the Dante approach, which allows semantic annotation of web pages to provide screen readers with extra (semantic) knowledge to better facilitate the audio presentation of a web page. Until now, such annotations were done manually, and failed for dynamic pages. In this paper, we combine the Dante approach with a web design method, WSDM, to fully automate the generation of the semantic annotation for visually impaired users. To do so, the semantic knowledge gathered during the design process is exploited, and the annotations are generated as a by-product of the design process, requiring no extra effort from the designer.