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

Información del proyecto

SmartCHANGE

Identificador del acuerdo de subvención: 101080965

DOI

10.3030/101080965

Fecha de la firma de la CE 20 Marzo 2023

Fecha de inicio 1 Mayo 2023

Fecha de finalización 30 Abril 2027

Financiado con arreglo a

• Health

Coste total

€ 5 967 395,00

Aportación de la UE

€ 5 967 395,00

Coordinado por

INSTITUT JOZEF STEFAN
Slovenia

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

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)

Publicaciones

Other (2)

AI in Healthcare: The AI Act and other Regulatory Issues

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

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

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

Conference proceedings (19)

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

Autores: Dominici, G.; Barbiero, P.; Giannini, Francesco; Gjoreski, M.; Langeinrich, M.
Publicado en: CoRR, 2024, ISSN 1613-0073
Editor: Cornell University -Arxiv
DOI: 10.48550/ARXIV.2405.16508

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

Autores: Dario Fenoglio, Mohan Li, Pietro Barbiero, Nicholas D. Lane, Marc Langheinrich, Martin Gjoreski
Publicado en: NeurIPS 2025, 2025
Editor: 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

Autores: Dario Fenoglio, Gabriele Dominici, Pietro Barbiero, Alberto Tonda, Martin Gjoreski, Marc Langheinrich
Publicado en: 2024
Editor: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2405.15632

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

Autores: Dario Fenoglio; Mohan Li; Davide Casnici; Matías Laporte; Shkurta Gashi; Silvia Santini; Martin Gjoreski; Marc Langheinrich
Publicado en: 2024 International Conference on Intelligent Environments (IE), 2024, ISSN 2469-8792
Editor: IEEE
DOI: 10.48550/ARXIV.2512.03287

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

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

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning

Autores: Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich
Publicado en: ICLR 2025, 2025
Editor: 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

Autores: Kramar Sebastjan, Jordan Marko, Založnik Marcel, Reščič Nina, Luštrek Mitja
Publicado en: 2024, ISSN 2545-4990
Editor: Zenodo
DOI: 10.5281/ZENODO.14549545

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

Publicado en: IS 2025 - Slovenian Conference on Artificial Intellignece, 2025
Editor: Jozef Stefan Institute
DOI: 10.70314/IS.2025.SKUI.7226

A Data Modeling Process for Achieving Interoperability

Autores: Eleftheria Kouremenou, Athanasios Kiourtis, Dimosthenis Kyriazis
Publicado en: 2024
Editor: Springer Nature
DOI: 10.5281/ZENODO.14541883

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

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

A Federated Unsupervised Personalisation for Cognitive Workload Estimation

Autores: Dario Fenoglio, Martin Gjoreski, Marc Langheinrich
Publicado en: Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia, 2023
Editor: ACM
DOI: 10.1145/3626705.3631796

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

Autores: 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
Publicado en: 2023 International Conference on Applied Mathematics & Computer Science (ICAMCS), 2024
Editor: IEEE
DOI: 10.1109/ICAMCS59110.2023.00020

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

Publicado en: Proceedings of Slovenian Conference on Artificial Intelligence 2024, 2024
Editor: Jožef Stefan Instutute
DOI: 10.70314/is.2024.scai.8844

Addressing Complex Data Integration and Harmonization Scenarios

Autores: University of Piraeus Research Center
Publicado en: 2024
Editor: Springer Nature
DOI: 10.5281/ZENODO.14541905

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

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

Counterfactual Concept Bottleneck Models

Autores: Dominici, G.; Barbiero, P.; Giannini, Francesco; Gjoreski, M.; Marra, G.; Langheinrich, M.
Publicado en: 2024
Editor: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2402.01408

A Data Modeling Process for Achieving Interoperability

Autores: Eleftheria Kouremenou, Athanasios Kiourtis, Dimosthenis Kyriazis
Editor: 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.

Autores: Claudia Dictus; Mai J.M. Chinapaw; Teatske M. Altenburg
Publicado en: Open Research Europe, 2025, ISSN 2732-5121
Editor: 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

Autores: Fawad Taj, J.M.M. Chinapaw, Teatske Altenburg, Tanja Vrijkotte
Publicado en: Journal of Exercise Science & Fitness, Edición 24, 2026, ISSN 1728-869X
Editor: Elsevier BV
DOI: 10.1016/J.JESF.2025.200431