Generative models for wearables data
This work addresses data shortages for medical researchers, enabling exploration of underrepresented populations while considering privacy, but it is incremental as it applies an existing method to a new domain.
The researchers tackled the problem of data scarcity in medical research by developing a multi-task self-attention model to generate realistic wearable activity data, achieving quantified similarity to genuine samples through both quantitative and qualitative evaluations.
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.