User Interface Abstraction for enabling TV set based Inclusive Access to the Information Society

199 p.The television (TV) set is present in most homes worldwide, and is the most used Information and Communication Technology (ICT). Despite its large implantation in the market, the interactive services consumption on TV set is limited. This thesis focuses on overcoming the following limiting factors: (i) limited Human Computer Interaction and (ii) lack of considering user’s real life context in the digital television (dTV) service integration strategy. Making interactive services accessible to TV set’s large user base, and especially to the most vulnerable ones, is understood as the path to integrate the mankind with the information society. This thesis explores the use of user interface abstraction technologies to reach the introduced goals. The main contributions of this thesis are: (i) an approach to enable the universally accessible remote control of the TV set, (ii) an approach for the provision of universally accessible interactive services through TV sets, and (iii) an approach for the provision of universally accessible services in the TV user’s real life context. We have implemented the contributing approaches for different use cases, and we have evaluated them with real users, achieving good results

COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations

The global population is aging in an unprecedented manner and the challenges for improving the lives of older adults are currently both a strong priority in the political and healthcare arena. In this sense, preventive measures and telemedicine have the potential to play an important role in improving the number of healthy years older adults may experience and virtual coaching is a promising research area to support this process. This paper presents COLAEVA, an interactive web application for older adult population clustering and evolution analysis. Its objective is to support caregivers in the design, validation and refinement of coaching plans adapted to specific population groups. COLAEVA enables coaching caregivers to interactively group similar older adults based on preliminary assessment data, using AI features, and to evaluate the influence of coaching plans once the final assessment is carried out for a baseline comparison. To evaluate COLAEVA, a usability test was carried out with 9 test participants obtaining an average SUS score of 71.1. Moreover, COLAEVA is available online to use and explore.This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 769830

User Interface Abstraction for enabling TV set based Inclusive Access to the Information Society

199 p.The television (TV) set is present in most homes worldwide, and is the most used Information and Communication Technology (ICT). Despite its large implantation in the market, the interactive services consumption on TV set is limited. This thesis focuses on overcoming the following limiting factors: (i) limited Human Computer Interaction and (ii) lack of considering user’s real life context in the digital television (dTV) service integration strategy. Making interactive services accessible to TV set’s large user base, and especially to the most vulnerable ones, is understood as the path to integrate the mankind with the information society. This thesis explores the use of user interface abstraction technologies to reach the introduced goals. The main contributions of this thesis are: (i) an approach to enable the universally accessible remote control of the TV set, (ii) an approach for the provision of universally accessible interactive services through TV sets, and (iii) an approach for the provision of universally accessible services in the TV user’s real life context. We have implemented the contributing approaches for different use cases, and we have evaluated them with real users, achieving good results

A secure data publishing and access service for sensitive data from Living Labs: enabling collaboration with external researchers via shareable data

Intending to enable a broader collaboration with the scientific community while maintaining privacy of the data stored and generated in Living Labs, this paper presents the Shareable Data Publishing and Access Service for Living Labs, implemented within the framework of the H2020 VITALISE project. Building upon previous work, significant enhancements and improvements are presented in the architecture enabling Living Labs to securely publish collected data in an internal and isolated node for external use. External researchers can access a portal to discover and download shareable data versions (anonymised or synthetic data) derived from the data stored across different Living Labs that they can use to develop, test, and debug their processing scripts locally, adhering to legal and ethical data handling practices. Subsequently, they may request remote execution of the same algorithms against the real internal data in Living Lab nodes, comparing the outcomes with those obtained using shareable data. The paper details the architecture, data flows, technical details and validation of the service with real-world usage examples, demonstrating its efficacy in promoting data-driven research in digital health while preserving privacy. The presented service can be used as an intermediary between Living Labs and external researchers for secure data exchange and to accelerate research on data analytics paradigms in digital health, ensuring compliance with data protection laws.This research was partly funded by the VITALISE (Virtual Health and Wellbeing Living Lab Infrastructure) project, funded by the Horizon 2020 Framework Program of the European Union for Research Innovation (grant agreement 101007990)

TAQIH, a tool for tabular data quality assessment and improvement in the context of health data

Background and objectives: Data curation is a tedious task but of paramount relevance for data analytics and more specially in the health context where data-driven decisions must be extremely accurate. The ambition of TAQIH is to support non-technical users on 1) the exploratory data analysis (EDA) process of tabular health data, and 2) the assessment and improvement of its quality. Methods A web-based tool has been implemented with a simple yet powerful visual interface. First, it provides interfaces to understand the dataset, to gain the understanding of the content, structure and distribution. Then, it provides data visualization and improvement utilities for the data quality dimensions of completeness, accuracy, redundancy and readability. Results It has been applied in two different scenarios. (1) The Northern Ireland General Practitioners (GPs) Prescription Data, an open data set containing drug prescriptions. (2) A glucose monitoring tele health system dataset. Findings on (1) include: Features that had significant amount of missing values (e.g. AMP_NM variable 53.39%); instances that have high percentage of variable values missing (e.g. 0.21% of the instances with > 75% of missing values); highly correlated variables (e.g. Gross and Actual cost almost completely correlated (∼ + 1.0)). Findings on (2) include: Features that had significant amount of missing values (e.g. patient height, weight and body mass index (BMI) (> 70%), date of diagnosis 13%)); highly correlated variables (e.g. height, weight and BMI). Full detail of the testing and insights related to findings are reported. Conclusions TAQIH enables and supports users to carry out EDA on tabular health data and to assess and improve its quality. Having the layout of the application menu arranged sequentially as the conventional EDA pipeline helps following a consistent analysis process. The general description of the dataset and features section is very useful for the first overview of the dataset. The missing value heatmap is also very helpful in visually identifying correlations among missing values. The correlations section has proved to be supportive as a preliminary step before further data analysis pipelines, as well as the outliers section. Finally, the data quality section provides a quantitative value to the dataset improvements. Keywords: Data quality; Exploratory data analysis; Data pre-processin