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Article

Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain

1
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastian, Spain
2
eHealth Group, Biodonostia Health Research Institute, 20014 Donostia-San Sebastian, Spain
3
Biomedical Engineering Department, Mondragon Unibertsitatea, 20500 Arrasate-Mondragon, Spain
4
Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
5
European Network of Living Labs, 1210 Brussels, Belgium
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: George Angelos Papadopoulos
Received: 28 January 2022 / Revised: 2 March 2022 / Accepted: 3 March 2022 / Published: 4 March 2022
(This article belongs to the Special Issue Recent Advances in Synthetic Data Generation)
To date, the use of synthetic data generation techniques in the health and wellbeing domain has been mainly limited to research activities. Although several open source and commercial packages have been released, they have been oriented to generating synthetic data as a standalone data preparation process and not integrated into a broader analysis or experiment testing workflow. In this context, the VITALISE project is working to harmonize Living Lab research and data capture protocols and to provide controlled processing access to captured data to industrial and scientific communities. In this paper, we present the initial design and implementation of our synthetic data generation approach in the context of VITALISE Living Lab controlled data processing workflow, together with identified challenges and future developments. By uploading data captured from Living Labs, generating synthetic data from them, developing analysis locally with synthetic data, and then executing them remotely with real data, the utility of the proposed workflow has been validated. Results have shown that the presented workflow helps accelerate research on artificial intelligence, ensuring compliance with data protection laws. The presented approach has demonstrated how the adoption of state-of-the-art synthetic data generation techniques can be applied for real-world applications. View Full-Text
Keywords: synthetic data generation; Living Lab; controlled data processing; machine learning synthetic data generation; Living Lab; controlled data processing; machine learning
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MDPI and ACS Style

Hernandez, M.; Epelde, G.; Beristain, A.; Álvarez, R.; Molina, C.; Larrea, X.; Alberdi, A.; Timoleon, M.; Bamidis, P.; Konstantinidis, E. Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain. Electronics 2022, 11, 812. https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11050812

AMA Style

Hernandez M, Epelde G, Beristain A, Álvarez R, Molina C, Larrea X, Alberdi A, Timoleon M, Bamidis P, Konstantinidis E. Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain. Electronics. 2022; 11(5):812. https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11050812

Chicago/Turabian Style

Hernandez, Mikel, Gorka Epelde, Andoni Beristain, Roberto Álvarez, Cristina Molina, Xabat Larrea, Ane Alberdi, Michalis Timoleon, Panagiotis Bamidis, and Evdokimos Konstantinidis. 2022. "Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain" Electronics 11, no. 5: 812. https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11050812

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