Generative models for wearables data

TL;DR

This paper introduces a multi-task self-attention generative model that produces realistic wearable activity data to address data scarcity in medical research, facilitating privacy-preserving data sharing and analysis.

Contribution

The paper presents a novel multi-task self-attention model specifically designed for synthesizing realistic wearable health data, a new approach in this domain.

Findings

Generated data closely resembles real samples quantitatively.

Qualitative analysis confirms realism of synthetic wearable data.

Model effectively captures key data characteristics.

Abstract

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.