Analysis Responses

How does a (smart) age-friendly ecosystem look in a post-pandemic society?

Category: User Experience

Journal paper: Abstract: COVID-19 has impacted not only the health of citizens, but also the various factors that make up our society, living environments, and ecosystems. This pandemic has shown that future living will need to be agile and flexible to adapt to the various changes in needs of societal populations. Digital technology has played an integral role during COVID-19, assisting various sectors of the community, and demonstrating that smart cities can provide opportunities to respond to many future societal challenges. In the decades ahead, the rise in aging populations will be one of these challenges, and one in which the needs and requirements between demographic cohorts will vary greatly. Although we need to create future smart age-friendly ecosystems to meet these needs, technology still does not feature in the WHO eight domains of an age-friendly city. This paper extends upon Marston and van Hoof's ?mart Age-friendly Ecosystem' (SAfE) framework, and explores how digital technology, design hacking, and research approaches can be used to understand a smart age-friendly ecosystem in a post-pandemic society. By exploring a series of case studies and using real-life scenarios from the standpoint of COVID-19, we propose the ?oncept of Age-friendly Smart Ecologies (CASE)' framework. We provide an insight into a myriad of contemporary multi-disciplinary research, which are capable to initiate discussions and bring various actors together with a positive impact on future planning and development of age-friendly ecosystems. The strengths and limitations of this framework are outlined, with advantages evident in the opportunity for towns, regions/counties, provinces, and states to take an agile approach and work together in adopting and implement improvements for the greater benefits of residents and citizens.

HUBBI: eHealth UsaBility Benchmarking instrument

Category: User Experience

Usability benchmarking tool for eHealth developed in the context of eHealth solutions for older adults

ICHOM Older Person set

Category: Standards

Standard set of KPIs or Patient Reported Outcomes (PRO) to assess the impact of technology solutions on older adult in diffent aspects.

Intelligent lighting system for comfortable living of the older people

Category: Projects

This research deals with the IoT-system development, that based on lighting and thermal comfortable parameters for improving the comfortable living of the older people. Also the second task of the IoT-system is avoiding and predicting instant decreasing of health level for older people based on data photoplethysmogram.IoT system uses the fuzzy knowledge base on the edge level for decreasing the computational complicity. For knowledge base development on cloud level the deep learning, clustering and fuzzy logic methods were used.

Learning modules Hands-on SHAFE BUILT

Category: User Experience

To learn to build inclusive environments we developed 7 online modules, including (among others) age-friendly house, dementia-friendly house, mobility outdoors.

Multimorbidity care model, JA-CHRODIS

Category: Taxonomies

An expert consensus meeting was held to develop a framework for care of multimorbid patients that can be applied across Europe, within a project funded by the European Union; the Joint Action on Chronic Diseases and Promoting Healthy Ageing across the Life Cycle (JA-CHRODIS). Sixteen components across five domains were identified (Delivery of Care; Decision Support; Self Management Support; Information Systems and Technology; and Social and Community Resources). The description and aim of each component are described in these guidelines, along with a summary of key characteristics and relevance to multimorbid patients.

Speech analysis for Hungtington or Parkinson (to detect possible signs of early impairment)

Category: Other

A model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson's disease (PD), Huntington's disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet)