@inproceedings{
kolbeinsson2023generative,
title={Generative models for wearables data},
author={Arinbj{\"o}rn Kolbeinsson and Luca Foschini},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=AGQYCBdKA5}
}
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.
Generative models for wearables data
Arinbjörn Kolbeinsson Luca Foschini