AI-based long-term health risk evaluation for driving behaviour change strategies in children and youth

Informations projet

SmartCHANGE

N° de convention de subvention: 101080965

DOI

10.3030/101080965

Date de signature de la CE 20 Mars 2023

Date de début 1 Mai 2023

Date de fin 30 Avril 2027

Financé au titre de

• Health

Coût total

€ 5 967 395,00

Contribution de l’UE

€ 5 967 395,00

Coordonné par

INSTITUT JOZEF STEFAN
Slovenia

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Data Management Plan (1)

Data management plan

Best practices and standards for project data, assessment of suitability for sharing

Documents, reports (3)

Initial dissemination, communication and exploitation plan

Plan for dissemination, communication and exploitation, including stakeholder groups and targeted engagement activities

SELP benchmark

A description of key ethical and legal requirements, and their frame

Initial technical specification

Initial specification of the design of the system architecture integrating all the components defined in the project to be used in the pilot study

Websites, patent fillings, videos etc. (1)

Project website

Website with project presentation, news and public materials (website and a short description)

Publications

Other (1)

AI in Healthcare: The AI Act and other Regulatory Issues

Auteurs: Favaro, John
DOI: 10.5281/ZENODO.13935246

Conference proceedings (10)

AnyCBMs How to Turn Any Black Box into a Concept Bottleneck Model

DOI: 10.5281/ZENODO.14582114

Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning

Auteurs: Dario Fenoglio, Gabriele Dominici, Pietro Barbiero, Alberto Tonda, Martin Gjoreski, Marc Langheinrich
Publié dans: 2024
Éditeur: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2405.15632

Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors

Auteurs: Dario Fenoglio, Mohan Li, Davide Casnici, Matias Laporte, Shkurta Gashi, Silvia Santini, Martin Gjoreski, Marc Langheinrich
Publié dans: 2024 International Conference on Intelligent Environments (IE), 2024
Éditeur: IEEE
DOI: 10.1109/IE61493.2024.10599924

Federated Learning for Privacy-aware Cognitive Workload Estimation

DOI: 10.1145/3626705.3627783

A Cross-Sector Data Space for Correlating Environmental Risks with Human Health

Auteurs: Athanasios Kiourtis, Argyro Mavrogiorgou, Dimosthenis Kyriazis
Éditeur: Springer Nature
DOI: 10.5281/ZENODO.14541845

A Federated Unsupervised Personalisation for Cognitive Workload Estimation

Auteurs: Dario Fenoglio, Martin Gjoreski, Marc Langheinrich
Publié dans: Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia, 2023
Éditeur: ACM
DOI: 10.1145/3626705.3631796

SmartCHANGE: AI-based long-term health risk evaluation for driving behaviour change strategies in children and youth

Auteurs: Nina Reščič, Janna Alberts, Teatske M. Altenburg, Mai J. M. Chinapaw, Antonio De Nigro, Dario Fenoglio, Martin Gjoreski, Anton Gradišek, Gregor Jurak, Athanasios Kiourtis, Dimosthenis Kyriazis, Marc Langheinrich, Elena Mancuso, Argyro Mavrogiorgou, Mykola Pechenizkiy, Roberto Pratola, José Ribeiro, Maroje Sorić, Fawad Taj, Tuija H. Tammelin, Martijn Vastenburg, Anna Vilanova, Tanja G. M. Vrijkotte, Mitja Luštrek
Publié dans: 2023 International Conference on Applied Mathematics & Computer Science (ICAMCS), 2024
Éditeur: IEEE
DOI: 10.1109/ICAMCS59110.2023.00020

SmartCHANGE Risk Prediction Tool: Demonstrating Risk Assessment for Children and Youth

Publié dans: Proceedings of Slovenian Conference on Artificial Intelligence 2024, 2024
Éditeur: Jožef Stefan Instutute
DOI: 10.70314/is.2024.scai.8844

Subgroup Harm Assessor: Identifying Potential Fairness-Related Harms andPredictive Bias

Auteurs: Adam Dubowski, Hilde Weerts, Anouk Wolters, Mykola Pechenizkiy
Publié dans: Lecture Notes in Computer Science, Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track, 2024
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-70371-3_31

A Data Modeling Process for Achieving Interoperability

Auteurs: Eleftheria Kouremenou, Athanasios Kiourtis, Dimosthenis Kyriazis
Éditeur: Springer Nature
DOI: 10.5281/ZENODO.14541883