As citizens’ age increases, smart cities must adapt to help them to age properly. The objective of the City4Age project is to create the future ambient assisted cities that will help the citizens to deal with mild cognitive impairments (MCI) and frailty. In this paper we present two of the tools developed during the project. The first one is a city-wide context-manager, which allows to store the citizens information using a semantic representation and share it following the linked open data paradigm. The second one are the individual care monitoring dashboards, which use the stored information to help the caregivers to analyze and interpret the citizens’ behaviour, allowing to detect risks related to MCI and frailty.

Since the start of the 21st century, the world has not confronted a more serious threat to global public health than the COVID-19 pandemic. While governments initially took radical actions in response to the pandemic to avoid catastrophic collapse of their health care systems, government policies have also had numerous knock-on socioeconomic, political, behavioral and economic effects. Researchers, thus, have a unique opportunity to forward our collective understanding of the modern world and to respond to the emergency situation in a way that optimizes resources and maximizes results. The PERISCOPE project, funded by the European Commission, brings together a large number of research institutions to collect data and carry out research to understand all the impacts of the pandemic, and create predictive models that can be used to optimize intervention strategies and better face possible future health emergencies. One of the main tangible outcomes of this project is the PERISCOPE Atlas: an interactive tool that allows to visualize and analyze COVID-19-related health, economic and sociopolitical data, featuring a WebGIS and several dashboards. This paper describes the first release of the Atlas, listing the data sources used, the main functionalities and the future development.

Amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are debilitating diseases with unpredictable progression. Artificial Intelligence-based tools for modelling disease progression could significantly improve the quality of life for patients and caregivers while supporting clinicians in delivering more personalized and timely care. However, the limited availability of data hinders the development, testing, and reproducibility of such predictive tools. To address this challenge, we curated, in the context of the H2020 BRAINTEASER project, four datasets containing clinical data from a total of 2,290 ALS patients and 723 MS patients. These datasets also include environmental data and information collected through wearable devices. Unlike most existing resources, the BRAINTEASER datasets are gathered from clinical practice, offering a more accurate representation of the data that an AI progression prediction tool would encounter in real-world scenarios. In addition to manual and automated data quality checks, the research community has validated the datasets through three editions of the intelligent Disease Progression Prediction challenges held within the Conference and Labs of the Evaluation Forum (CLEF).

The presented demonstrated working tools in the initial version constitute the foundation of the novel ALS and MS management and monitoring, leveraging extended IoT sensing and emerging instruments infrastructure, and a basis for integration of more advanced and effective AI models (in development) for disease progression prediction, patient stratification and ambiental exposure assessment.

We present the grounding approach, deployment and preliminary validation of the elementary devised model of physical well-being in urban environments, summarizing the heterogeneous personal Big Data (on physical activity/exercise, walking, cardio-respiratory fitness, quality of sleep and related lifestyle and health habits and status, continuously collected for over a year mainly through wearable IoT devices and survey instruments in 7 global testbed cities) into 5 composite domain indicators/indexes convenient for interpretation and use in predictive public health and preventive interventions. The approach is based on systematized comprehensive domain knowledge implemented through range/threshold-based rules from institutional and study recommendations, combined with statistical methods, and will serve as a representative and performance benchmark for evolution and evaluation of more complex and advanced well-being models for the aimed predictive analytics (incorporating machine learning methods) in subsequent development underway.