Synthetic Data Generation for Physical Activity in Wearable Devices: A Multivariate Time Series Approach

Synthetic data generation is an emerging solution to address current data privacy and availability challenges, especially in healthcare applications and wearable devices. This study aims to generate synthetic multivariate time-series data that simulates physical activities, preserving the variability and dependencies observed in real-world data (RWD). An innovative approach that includes data organization, preprocessing, and model training on a dataset of 100 users with several datetime, numeric, and categorical variables is conducted. By using two artificial intelligence (AI) models — Periodic Autoregressive (PAR) and Conditional Tabular Generative Adversarial Network (CTGAN)—a total of 100 new synthetic users with temporal and feature-specific activity patterns were generated. Evaluation results showed strong alignment between synthetic and original data, with mean values of 0.014 for Kolmogorov-Smirnov (KS) Test, 0.057 for Jensen-Shannon (JS) Distance, and 0.011 for Pairwise Correlation, indicating realistic data relationships and feature distributions. Despite these preliminary results, future work includes enhancing computational efficiency and scalability, expanding its generalizability across diverse datasets, and further validation.