![]() In this context, synthetic data generation (SDG) has been widely researched within health and wellbeing domains for different data types, including biomedical signals, medical images, time-series smart-home activity data, and EHR tabular data. Research on health and wellbeing-related SD has gained importance in recent years due to the lack of sufficient RD (both in terms of access and availability) for artificial intelligence (AI) and machine learning (ML) model development. Synthetic data (SD) is data generated artificially by a mathematical model to replicate distributions and structures of some real data (RD). The presented approach has demonstrated how the adoption of state-of-the-art synthetic data generation techniques can be applied for real-world applications. Results have shown that the presented workflow helps accelerate research on artificial intelligence, ensuring compliance with data protection laws. 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. 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. 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. ![]() ![]() 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. ![]() To date, the use of synthetic data generation techniques in the health and wellbeing domain has been mainly limited to research activities. ![]()
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