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

Informazioni relative al progetto

SmartCHANGE

ID dell’accordo di sovvenzione: 101080965

DOI

10.3030/101080965

Data della firma CE 20 Marzo 2023

Data di avvio 1 Maggio 2023

Data di completamento 30 Aprile 2027

Finanziato da

• Health

Costo totale

€ 5 967 395,00

Contributo UE

€ 5 967 395,00

Coordinato da

INSTITUT JOZEF STEFAN
Slovenia

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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)

Pubblicazioni

Other (2)

AI in Healthcare: The AI Act and other Regulatory Issues

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

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

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

Conference proceedings (29)

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

Autori: Dominici, G.; Barbiero, P.; Giannini, Francesco; Gjoreski, M.; Langeinrich, M.
Pubblicato in: CoRR, 2024, ISSN 1613-0073
Editore: Cornell University -Arxiv
DOI: 10.48550/ARXIV.2405.16508

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

Autori: Dario Fenoglio, Mohan Li, Pietro Barbiero, Nicholas D. Lane, Marc Langheinrich, Martin Gjoreski
Pubblicato in: NeurIPS 2025, 2025
Editore: Cornell Univeristy - Arxiv
DOI: 10.48550/ARXIV.2511.22305

Towards Transparent Reasoning: What Drives Faithfulness in Large Language Models?

Autori: Teague McMillan, Gabriele Dominici, Martin Gjoreski, Marc Langheinrich
Pubblicato in: NeurIPS 2025, 2025
Editore: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2510.24236

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

Autori: Dario Fenoglio, Gabriele Dominici, Pietro Barbiero, Alberto Tonda, Martin Gjoreski, Marc Langheinrich
Pubblicato in: 2024
Editore: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2405.15632

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

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

Federated Learning with Profile Mapping under Distribution Shifts and Drifts

Autori: Mohan Li, Dario Fenoglio, Martin Gjoreski, Marc Langheinrich
Pubblicato in: ICLR 2026, 2026
Editore: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2602.07671

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

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

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning

Autori: Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich
Pubblicato in: ICLR 2025, 2025
Editore: 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

Autori: Kramar Sebastjan, Jordan Marko, Založnik Marcel, Reščič Nina, Luštrek Mitja
Pubblicato in: 2024, ISSN 2545-4990
Editore: Zenodo
DOI: 10.5281/ZENODO.14549545

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

Pubblicato in: IS 2025 - Slovenian Conference on Artificial Intellignece, 2025
Editore: Jozef Stefan Institute
DOI: 10.70314/IS.2025.SKUI.7226

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts

Autori: Mateo Espinosa Zarlenga, Gabriele Dominici, Pietro Barbiero, Zohreh Shams, Mateja Jamnik
Pubblicato in: ICML 2025, 2025
Editore: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2504.17921

Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering

Autori: Francesco Sovrano, Gabriele Dominici, Alberto Bacchelli
Pubblicato in: FSE 2026, 2026
Editore: Cornell University - Arxiv
DOI: 10.1145/3808115

A Data Modeling Process for Achieving Interoperability

Autori: Eleftheria Kouremenou, Athanasios Kiourtis, Dimosthenis Kyriazis
Pubblicato in: 2024
Editore: Springer Nature
DOI: 10.5281/ZENODO.14541883

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

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

A Federated Unsupervised Personalisation for Cognitive Workload Estimation

Autori: Dario Fenoglio, Martin Gjoreski, Marc Langheinrich
Pubblicato in: Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia, 2023
Editore: ACM
DOI: 10.1145/3626705.3631796

Federated Learning for Privacy-aware Cognitive Workload Estimation

Autori: Dario Fenoglio, Daniel Josifovski, Alessandro Gobbetti, Mattias Formo, Hristijan Gjoreski, Martin Gjoreski, Marc Langheinrich
Pubblicato in: Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia, 2025
Editore: ACM
DOI: 10.1145/3626705.3627783

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

Autori: 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
Pubblicato in: 2023 International Conference on Applied Mathematics & Computer Science (ICAMCS), 2024
Editore: IEEE
DOI: 10.1109/ICAMCS59110.2023.00020

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

Pubblicato in: Proceedings of Slovenian Conference on Artificial Intelligence 2024, 2024
Editore: Jožef Stefan Instutute
DOI: 10.70314/is.2024.scai.8844

Addressing Complex Data Integration and Harmonization Scenarios

Autori: University of Piraeus Research Center
Pubblicato in: 2024
Editore: Springer Nature
DOI: 10.5281/ZENODO.14541905

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

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

Counterfactual Concept Bottleneck Models

Autori: Dominici, G.; Barbiero, P.; Giannini, Francesco; Gjoreski, M.; Marra, G.; Langheinrich, M.
Pubblicato in: 2024
Editore: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2402.01408

Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation

Autori: Francesco Sovrano, Gabriele Dominici, Marc Langheinrich
Pubblicato in: KDD 2026, 2026
Editore: Cornell University - Arxiv
DOI: 10.1145/3770855.3818091

Federated Learning with Energy-Based Structured Probabilistic Inference

Autori: Dario Fenoglio, Daniil Kirilenko, Martin Gjoreski, Marc Langheinrich
Pubblicato in: ICML 2026, 2026
Editore: OpenReview.net

A Data Modeling Process for Achieving Interoperability

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

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

Autori: Nina Reščič, Marko Jordan, Sebastjan Kramar, Ana Krstevska, Marcel Založnik, Lotte van der Jagt, Harm op den Akker, Martijn Vastenburg, Valentina Di Giacomo, Elena Mancuso, Dario Fenoglio, Gabriele Dominici, Mitja Luštrek
Pubblicato in: IS 2025 - Slovenian Conference on Artificial Intellignece, 2025
Editore: Jozef Stefan Institute
DOI: 10.70314/IS.2025.SKUI.7226

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

Autori: Marko Jordan, Nina Reščič, Sebastjan Kramar, Marcel Založnik and Mitja Luštrek
Pubblicato in: Proceedings of Slovenian Conference on Artificial Intelligence 2024
Editore: Jožef Stefan Institute
DOI: 10.70314/IS.2024.SCAI.8844

Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity

Autori: Qiao Xiao; Boqian Wu; Andrey Poddubnyy; Elena Mocanu; Phuong H. Nguyen; Mykola Pechenizkiy; Decebal Constantin Mocanu
Pubblicato in: CoRR, 2025
Editore: Cornell University - Arxiv
DOI: 10.48550/ARXIV.2506.00932

Peer reviewed articles (3)

Comparison of Three Anonymization Tools for a Health Fitness Study

Autori: Paul Francis; Gregor Jurak; Bojan Leskošek; Karen Otte; Fabian Prasser
Pubblicato in: Scientific Data, 2025, ISSN 2052-4463
Editore: Nature - Scientific Data
DOI: 10.1038/S41597-025-05823-X

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

Autori: Claudia Dictus; Mai J.M. Chinapaw; Teatske M. Altenburg
Pubblicato in: Open Research Europe, 2025, ISSN 2732-5121
Editore: 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

Autori: Fawad Taj, J.M.M. Chinapaw, Teatske Altenburg, Tanja Vrijkotte
Pubblicato in: Journal of Exercise Science & Fitness, Numero 24, 2026, ISSN 1728-869X
Editore: Elsevier BV
DOI: 10.1016/J.JESF.2025.200431