Survey on virtual coaching for older adults

Virtual coaching has emerged as a promising solution to extend independent living for older adults. A virtual coach system is an always-attentive personalized system that continuously monitors user's activity and surroundings and delivers interventions - that is, intentional messages - in the appropriate moment. This article presents a survey of different approaches in virtual coaching for older adults, from the less technically supported tools to the latest developments and future avenues for research. It focuses on the technical aspects, especially on software architectures, user interaction and coaching personalization. Nevertheless, some aspects from the fields of personality/social psychology are also presented in the context of coaching strategies. Coaching is considered holistically, including matters such as physical and cognitive training, nutrition, social interaction and mood.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 769830

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

Reconocimiento visual de patrones basado en esqueletos: aplicaciones a la interacción en mesas computacionales

211 p. :il.[EN]The practical context of this thesis work is natural interaction in Interactive Multimedia Tabletops using visually recognized hand gestures. An interaction language has been de ned for the speci c characteristics of this kind of systems and applications. Hand gestures are captured using an optical video camera, then the image is processed to obtain the hand shape skeleton. Gesture recognition is performed using pattern matching techniques speci cally designed to work on the information provided by skeletal representations of the shapes in the image. This thesis work introduces a theoretical and computational contribution to the skeleton computation procedure which achieves better stability than other algorithms in the literature. This contribution starts from Voronoi skeleton computation method and based in results which permit an e cient and robust pruning, obtains stable skeletons in realtime (60fps), which make possible the application of this technique in the context of Interactive Multimedia Tabletops[ES]El contexto de aplicación de esta tesis es la interacción en mesas computacionales (tabletops) mediante gestos manuales reconocidos visualmente. Se ha definido un lenguaje de interacción conveniente para este tipo de sistemas y aplicaciones. Los gestos de la mano son capturados con una cámara óptica, la imagen es procesada hasta obtener el esqueleto de la mano. El reconocimiento de los gestos se realiza mediante técnicas de reconocimiento de patrones especialmente diseñadas para trabajar sobre representaciones dadas por información extraída del esqueleto de la figura en la imagen. La tesis presenta una aportación teórica y computacional en el proceso de cálculo del esqueleto que consigue mayor estabilidad que otros algoritmos en la literatura. Dicha aportación parte del método de Voronoi de construcción de esqueletos y en base a resultados que permiten una poda eficiente y estable, obtiene esqueletos estables en tiempo real (30fps) que hacen posible la aplicación de esta técnica en el contexto de las mesas computacionales

Correction: Beristain Iraola et al. User Centered Virtual Coaching for Older Adults at Home Using SMART Goal Plans and I-Change Model. Int. J. Environ. Res. Public Health 2021, 18, 6868

The author would like to change the authorship in the previous publication [...

Reconocimiento visual de patrones basado en esqueletos: aplicaciones a la interacción en mesas computacionales

211 p. :il.[EN]The practical context of this thesis work is natural interaction in Interactive Multimedia Tabletops using visually recognized hand gestures. An interaction language has been de ned for the speci c characteristics of this kind of systems and applications. Hand gestures are captured using an optical video camera, then the image is processed to obtain the hand shape skeleton. Gesture recognition is performed using pattern matching techniques speci cally designed to work on the information provided by skeletal representations of the shapes in the image. This thesis work introduces a theoretical and computational contribution to the skeleton computation procedure which achieves better stability than other algorithms in the literature. This contribution starts from Voronoi skeleton computation method and based in results which permit an e cient and robust pruning, obtains stable skeletons in realtime (60fps), which make possible the application of this technique in the context of Interactive Multimedia Tabletops[ES]El contexto de aplicación de esta tesis es la interacción en mesas computacionales (tabletops) mediante gestos manuales reconocidos visualmente. Se ha definido un lenguaje de interacción conveniente para este tipo de sistemas y aplicaciones. Los gestos de la mano son capturados con una cámara óptica, la imagen es procesada hasta obtener el esqueleto de la mano. El reconocimiento de los gestos se realiza mediante técnicas de reconocimiento de patrones especialmente diseñadas para trabajar sobre representaciones dadas por información extraída del esqueleto de la figura en la imagen. La tesis presenta una aportación teórica y computacional en el proceso de cálculo del esqueleto que consigue mayor estabilidad que otros algoritmos en la literatura. Dicha aportación parte del método de Voronoi de construcción de esqueletos y en base a resultados que permiten una poda eficiente y estable, obtiene esqueletos estables en tiempo real (30fps) que hacen posible la aplicación de esta técnica en el contexto de las mesas computacionales

Prediction and Analysis of Heart Failure Decompensation Events Based on Telemonitored Data and Artificial Intelligence Methods

Cardiovascular diseases are the leading cause of death globally, taking an estimated 17.9 million lives each year. Heart failure (HF) occurs when the heart is not able to pump enough blood to satisfy metabolic needs. People diagnosed with chronic HF may suffer from cardiac decompensation events (CDEs), which cause patients’ worsening. Being able to intervene before decompensation occurs is the major challenge addressed in this study. The aim of this study is to exploit available patient data to develop an artificial intelligence (AI) model capable of predicting the risk of CDEs timely and accurately. Materials and Methods: The vital variables of patients (n = 488) diagnosed with chronic heart failure were monitored between 2014 and 2022. Several supervised classification models were trained with these monitoring data to predict CDEs, using clinicians’ annotations as the gold standard. Feature extraction methods were applied to identify significant variables. Results: The XGBoost classifier achieved an AUC of 0.72 in the cross-validation process and 0.69 in the testing set. The most predictive physiological variables for CAE decompensations are weight gain, oxygen saturation in the final days, and heart rate. Additionally, the answers to questionnaires on wellbeing, orthopnoea, and ankles are strongly significant predictors

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

First Workshop on Multimodal e-Coaches

This preprint follows ACM policy: “Authors who publish with ACM have the freedom to post peer-reviewed pre-print versions of their papers to personal websites and institutional repositories. They can add a single-click link to their final published papers, and re-use any portion of their published work with the inclusion of a citation and DOI link. Authors can also post on any repository legally mandated by the agency funding the research on which the work is based, and on any non-commercial repository or aggregation that does not duplicate ACM tables of contents/substantially duplicate an ACM-copyrighted volume or issue” (https://authors.acm.org/author-resources/author-rights.) 2e-Coaches are promising intelligent systems that aims at supporting human everyday life, dispatching advice through different interfaces, such as apps, conversational interfaces and augmented reality interfaces. This workshop aims at exploring how e-coaches might benefit from spatially and timemultiplexed interfaces and from different communication modalities (e.g., text, visual, audio, etc.) according to the context of the interaction.The NESTORE, SAAM, CAPTAIN, HOLOBALANCE, EMPATHIC, MENHIR, vCare projects are supported by the European Commission under the Horizon 2020 programmes SC1-PM-15-2017 H2020-MSCA-RISE-2018, and H2020-1.3.3, respectively through the project grants N.769643, 769661, 769830, 769574, 769872, 823907, 769807. The authors want to thank their respective Consortia. The opinions expressed in this paper are those of the authors and are not necessarily those of the project partners or the European Commission