Designing a gamified social platform for people living with dementia and their live-in family caregivers

In the current paper, a social gamified platform for people living with dementia and their live-in family caregivers, integrating a broader diagnostic approach and interactive interventions is presented. The CAREGIVERSPRO-MMD (C-MMD) platform constitutes a support tool for the patient and the informal caregiver - also referred to as the dyad - that strengthens self-care, and builds community capacity and engagement at the point of care. The platform is implemented to improve social collaboration, adherence to treatment guidelines through gamification, recognition of progress indicators and measures to guide management of patients with dementia, and strategies and tools to improve treatment interventions and medication adherence. Moreover, particular attention was provided on guidelines, considerations and user requirements for the design of a User-Centered Design (UCD) platform. The design of the platform has been based on a deep understanding of users, tasks and contexts in order to improve platform usability, and provide adaptive and intuitive User Interfaces with high accessibility. In this paper, the architecture and services of the C-MMD platform are presented, and specifically the gamification aspects. © 2018 Association for Computing Machinery.Peer ReviewedPostprint (author's final draft

Reducing fall risk with combined motor and cognitive training in elderly fallers

Background. Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities. The aim was to assess the effectiveness of a motor and a cognitive treatment developed within the EU co-funded project I-DONT-FALL. Methods. In a sample of 481 elderly people at risk of falls recruited in this multicenter randomised controlled trial, the effectiveness of a motor treatment (pure motor or mixed with EFt) of 24 one-hour sessions delivered through an i-Walker with a non-motor treatment (pure EFt or control condition) was evaluated. Similarly, a 24 one-hour session cognitive treatment (pure EFt or mixed with motor training), delivered through a touch-screen computer was compared with a non-cognitive treatment (pure motor or control condition). Results. Motor treatment, particularly when mixed with EFt, reduced significantly fear of falling (F(1,478) = 6.786, p = 0.009) although to a limited extent (ES -0.25) restricted to the period after intervention. Conclusions. This study suggests the effectiveness of motor treatment empowered by EFt in reducing fear of falling.Peer ReviewedPostprint (published version

Introducing social robots to assess frailty in older adults

Frailty is a crucial indicator in determining the well-being of older adults in terms of their health. With the growing number of elderly people, the demand for geriatricians is increasing, which means that they have less time to spend with each patient. The current methods for frailty assessment use simple tests that are time-consuming and do not require specific medical expertise. To address this issue, this paper proposes the use of social robots to assess frailty autonomously. It presents a practical proposal that defines the robot’s behavior and explains the design and implementation concepts. Finally, it discusses some of the challenges that may arise from introducing social robots as frailty evaluators.This work was supported by the project ROB-IN PLEC2021-007859 funded by MCIN/ AEI /10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR"; project CHLOE-GRAPH PID2020-118649RB-I00 funded by MCIN/AEI /10.13039/501100011033; and the 23S06141-001 FRAILWATCH project funded by Barcelona Ciencia 2020-2023 Plan.Peer ReviewedPostprint (published version

Cognitive and emotional predictors of quality of life and functioning after COVID-19

Quality of life; Cognitive and emotional predictors; COVID-19Calidad de vida; Predictores cognitivos y emocionales; COVID-19Qualitat de vida; Predictors cognitius i emocionals; COVID-19Objective: A long-term decline in health-related quality of life (HRQoL) has been reported after coronavirus disease 2019 (COVID-19). Studies with people with persistent symptoms showed inconsistent outcomes. Cognition and emotion are important determinants in HRQoL, but few studies have examined their prognostic significance for HRQoL and functionality in post-COVID patients with persisting symptoms. We aimed to describe QoL, HRQoL, and functioning in individuals post-COVID with varying COVID-19 severities and to investigate the predictive value of cognitive and emotional variables for QoL, HRQoL, and functioning. Methods: In total, 492 participants (398 post-COVID and 124 healthy controls) underwent a neurobehavioral examination that included assessments of cognition, mood, QoL/HRQoL (WHOQOL-BREF, EQ-5D), and functioning (WHODAS-II). Analysis of covariance and linear regression models were used to study intergroup differences and the relationship between cognitive and emotional variables and QoL and functioning. Results: The Physical and Psychological dimensions of WHOQoL, EQ-5D, and WHODAS Cognition, Mobility, Life Activities, and Participation dimensions were significantly lower in post-COVID groups compared with a control group. Regression models explaining 23.9%-53.9% of variance were obtained for the WHOQoL-BREF dimensions and EQ-5D, with depressive symptoms, post-COVID symptoms, employment status, income, and mental speed processing as main predictors. For the WHODAS, models explaining 17%-60.2% of the variance were obtained. Fatigue, depressive symptoms, mental speed processing, and post-COVID symptoms were the main predictors. Interpretation: QoL/HRQoL and functioning after COVID-19 in individuals with persistent symptoms were lower than in non-affected persons. Depressive symptoms, fatigue, and slower mental processing speed were predictors of lower QoL/HRQoL and functioning.This research was supported by the Agency for Management of University and Research Grants (AGAUR) from the Generalitat de Catalunya (Pandemies, 202PANDE 00053) and La Marato de TV3 Foundation (202111-30- 31-32)

Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19

Post-COVID-19 condition; Mental speed processing; Logistic regressionCondición post-COVID-19; Procesamiento de velocidad mental; Regresión logísticaEstat post-COVID-19; Processament de velocitat mental; Regressió logísticaThe risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was supported by the European Archives of Psychiatry and Clinical Neuroscience Agency for Management of University and Research Grants (AGAUR) from the Generalitat de Catalunya (Pandemies, 2020PANDE00053), the La Marató de TV3 Foundation (202111–30-31–32), the Ministerio de Ciencia e Innovación (TED2021-130409B-C55)

An adaptive scheme for wheelchair navigation collaborative control

In this paper we propose a system where machine and human cooperate at every situation via a reactive emergent behavior, so that the person is always in charge of his/her own motion. Our approach relies on locally evaluating the performance of the human and the wheelchair for each given situation. Then, both their motion commands are weighted according to those efficiencies and combined in a reactive way. This approach benefits from the advantages of typical reactive behaviors to combine different sources of information in a simple, seamless way into an emergent trajectory.Peer ReviewedPostprint (author’s final draft

Health recommender system design in the context of CAREGIVERSPRO-MMD project

CAREGIVERSPRO-MMD an EU H2020 funded project aims to build a digital platform focusing on people living with dementia and their caregivers, offering a selection of advanced, individually tailored services enabling them to live well in the community for as long as possible. This paper provides an outline of a health recommender system designed in the context of the project to provide tailored interventions to caregivers and people living with dementia.Peer ReviewedPostprint (published version

knowlEdge Project –Concept, methodology and innovations for artificial intelligence in industry 4.0

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations.The research leading to these results has received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 957331 for EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop].Peer ReviewedTreball signat per 21 autors/autores: Sergio Alvarez-Napagao, Barcelona Supercomputing Center, Spain; Boki Ashmore, ICE, United Kingdom; Marta Barroso, Barcelona Supercomputing Center, Spain; Cristian Barrué, Barcelona Supercomputing Center, Spain; Christian Beecks, University of Münster, Germany; Fabian Berns, University of Münster, Germany; Ilaria Bosi, LINKS Foundation, Italy; Sisay Adugna Chala, Fraunhofer FIT, Germany; Nicola Ciulli, Nextworks, Italy; Marta Garcia-Gasulla, Barcelona Supercomputing Center, Spain; Alexander Grass, Fraunhofer FIT, Germany; Dimosthenis Ioannidis, CERTH/ITI, Greece; Natalia Jakubiak, Universitat Politècnica de Catalunya, Spain; Karl Köpke, Kautex Textron, Germany; Ville Lämsä, VTT Technical Research Centre, Finland; Pedro Megias, Barcelona Supercomputing Center, Spain; Alexandros Nizamis, CERTH/ITI, Greece; Claudio Pastrone, LINKS Foundation, Italy; Rosaria Rossini, LINKS Foundation, Italy; Miquel Sànchez-Marrè, Universitat Politècnica de Catalunya, Spain; Luca Ziliotti, Parmalat, ItalyPostprint (author's final draft

A new collaborative shared control strategy for continuous elder/robot assisted navigation

In nowadays aging society, many people require mobility assistance. Autonomous wheelchairs may provide some help, but they are not supposed to overtake all control on human mobility, as this is reported to lead to loss of residual capabilities and frustration. Instead, persons and wheelchairs are expected to cooperate. Traditionally, shared control hands control from human to robot depending on a triggering event. In this paper, though, we propose a method to allow constant cooperation between humans and robots, so that both have some weight in the emergent navigating behavior. We have tested the proposed method on a robotized Meyra wheelchair at Santa Lucia Hospedale in Rome with several volunteering in-patients presenting different disabilities. Results in indoor environments have been satisfactory both from a quantitative and qualitative point of view.Peer ReviewedPostprint (author’s final draft

COVID-19 severity is related to poor executive function in people with post-COVID conditions

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer NaturePatients with post-coronavirus disease 2019 (COVID-19) conditions typically experience cognitive problems. Some studies have linked COVID-19 severity with long-term cognitive damage, while others did not observe such associations. This discrepancy can be attributed to methodological and sample variations. We aimed to clarify the relationship between COVID-19 severity and long-term cognitive outcomes and determine whether the initial symptomatology can predict long-term cognitive problems. Cognitive evaluations were performed on 109 healthy controls and 319 post-COVID individuals categorized into three groups according to the WHO clinical progression scale: severe-critical (n¿=¿77), moderate-hospitalized (n¿=¿73), and outpatients (n¿=¿169). Principal component analysis was used to identify factors associated with symptoms in the acute-phase and cognitive domains. Analyses of variance and regression linear models were used to study intergroup differences and the relationship between initial symptomatology and long-term cognitive problems. The severe-critical group performed significantly worse than the control group in general cognition (Montreal Cognitive Assessment), executive function (Digit symbol, Trail Making Test B, phonetic fluency), and social cognition (Reading the Mind in the Eyes test). Five components of symptoms emerged from the principal component analysis: the “Neurologic/Pain/Dermatologic” “Digestive/Headache”, “Respiratory/Fever/Fatigue/Psychiatric” and “Smell/ Taste” components were predictors of Montreal Cognitive Assessment scores; the “Neurologic/Pain/Dermatologic” component predicted attention and working memory; the “Neurologic/Pain/Dermatologic” and “Respiratory/Fever/Fatigue/Psychiatric” components predicted verbal memory, and the “Respiratory/Fever/Fatigue/Psychiatric,” “Neurologic/Pain/Dermatologic,” and “Digestive/Headache” components predicted executive function. Patients with severe COVID-19 exhibited persistent deficits in executive function. Several initial symptoms were predictors of long-term sequelae, indicating the role of systemic inflammation and neuroinflammation in the acute-phase symptoms of COVID-19.” Study Registration: www.ClinicalTrials.gov, identifier NCT05307549 and NCT05307575.This research was supported by the Agency for Management of University and Research Grants (AGAUR) from the Generalitat de Catalunya (Pandemies, 202PANDE00053) and La Marató de TV3 Foundation (202111-30-31-32).Peer ReviewedArticle signat per 16 autors/es: Mar Ariza, Neus Cano, Bàrbara Segura, Ana Adan, Núria Bargalló, Xavier Caldú, Anna Campabadal, Maria Angeles Jurado, Maria Mataró, Roser Pueyo, Roser Sala‑Llonch, Cristian Barrué, Javier Bejar, Claudio Ulises Cortés on behalf of NAUTILUS Project Collaborative Group, Maite Garolera Carme JunquéPostprint (published version