Generative models for wearables data
Arinbjörn Kolbeinsson Luca Foschini
Data scarcity is a common obstacle in medical research due to the high costs
associated with data collection and the complexity of gaining access to and
utilizing data. Synthesizing health data may provide an efficient and
cost-effective solution to this shortage, enabling researchers to explore
distributions and populations that are not represented in existing observations
or difficult to access due to privacy considerations. To that end, we have
developed a multi-task self-attention model that produces realistic wearable
activity data. We examine the characteristics of the generated data and
quantify its similarity to genuine samples with both quantitative and
qualitative approaches.