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 (2)

AI in Healthcare: The AI Act and other Regulatory Issues

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

SmartCHANGE Webinar - Putting Health Tools into practice: Designing feasibility studies at an international scale

Auteurs: Domenici, Diego Dylan; op den Akker, Harm; Di Giacomo, Valentina; Jurak, Gregor; Tammelin, Tuija; Laaksonen, Heikki; Dictus, Claudia; Hoogsteder, Mariëtte; Ribeiro, Jose; Bomrah, Sherali
Publié dans: 2025
Éditeur: Zenodo
DOI: 10.5281/ZENODO.17581364

Conference proceedings (19)

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

Auteurs: Dominici, G.; Barbiero, P.; Giannini, Francesco; Gjoreski, M.; Langeinrich, M.
Publié dans: CoRR, 2024, ISSN 1613-0073
Éditeur: Cornell University -Arxiv
DOI: 10.48550/ARXIV.2405.16508

FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts

Auteurs: Dario Fenoglio, Mohan Li, Pietro Barbiero, Nicholas D. Lane, Marc Langheinrich, Martin Gjoreski
Publié dans: NeurIPS 2025, 2025
Éditeur: Cornell Univeristy - Arxiv
DOI: 10.48550/ARXIV.2511.22305

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; Matías Laporte; Shkurta Gashi; Silvia Santini; Martin Gjoreski; Marc Langheinrich
Publié dans: 2024 International Conference on Intelligent Environments (IE), 2024, ISSN 2469-8792
Éditeur: IEEE
DOI: 10.48550/ARXIV.2512.03287

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

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning

Auteurs: Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich
Publié dans: ICLR 2025, 2025
Éditeur: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2402.01408

Federated Learning for Privacy-aware Cognitive Workload Estimation

DOI: 10.1145/3626705.3627783

Health Risk Prediction for Children Built on a Combination of Heterogeneous Datasets

Auteurs: Kramar Sebastjan, Jordan Marko, Založnik Marcel, Reščič Nina, Luštrek Mitja
Publié dans: 2024, ISSN 2545-4990
Éditeur: Zenodo
DOI: 10.5281/ZENODO.14549545

SmartCHANGE Risk Prediction Tool: Next-Generation RiskAssessment for Children and Youth

Publié dans: IS 2025 - Slovenian Conference on Artificial Intellignece, 2025
Éditeur: Jozef Stefan Institute
DOI: 10.70314/IS.2025.SKUI.7226

A Data Modeling Process for Achieving Interoperability

Auteurs: Eleftheria Kouremenou, Athanasios Kiourtis, Dimosthenis Kyriazis
Publié dans: 2024
Éditeur: Springer Nature
DOI: 10.5281/ZENODO.14541883

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

Addressing Complex Data Integration and Harmonization Scenarios

Auteurs: University of Piraeus Research Center
Publié dans: 2024
Éditeur: Springer Nature
DOI: 10.5281/ZENODO.14541905

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

Counterfactual Concept Bottleneck Models

Auteurs: Dominici, G.; Barbiero, P.; Giannini, Francesco; Gjoreski, M.; Marra, G.; Langheinrich, M.
Publié dans: 2024
Éditeur: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2402.01408

A Data Modeling Process for Achieving Interoperability

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

Peer reviewed articles (2)

Navigating multi-country, multi-disciplinary co-design with families, adolescents and healthcare practitioners: the SmartCHANGE project.

Auteurs: Claudia Dictus; Mai J.M. Chinapaw; Teatske M. Altenburg
Publié dans: Open Research Europe, 2025, ISSN 2732-5121
Éditeur: Open Research Europe
DOI: 10.12688/OPENRESEUROPE.21004.1

Prospective association between descriptive accelerometer-derived physical behaviour metrics and cardiometabolic risk indicators in Dutch children: The ABCD study

Auteurs: Fawad Taj, J.M.M. Chinapaw, Teatske Altenburg, Tanja Vrijkotte
Publié dans: Journal of Exercise Science & Fitness, Numéro 24, 2026, ISSN 1728-869X
Éditeur: Elsevier BV
DOI: 10.1016/J.JESF.2025.200431