arXiv AI October 5, 2026 By E. Riveros (Institute of Computing, State University of Campinas, Campinas, Brazil), D. Vega-Oliveros (Institute of Science and Technology, Federal University of Sao Paulo, Sao Jose dos Campos, Brazil), A. Soriano-Vargas (Universidad de Ingenieria y Tecnologia, Lima, Peru), A. Rocha (Institute of Computing, State University of Campinas, Campinas, Brazil)

Diffusion-Based Synthetic Data Pretraining for Enhancing Activity Recognition

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The paper proposes a diffusion-based synthetic data pretraining approach to improve human activity recognition (HAR) for dietary behaviors. It builds on the CABiGRU architecture, pre‑training it with synthetic sensor windows generated by a diffusion model and then fine‑tuning on real data. On the DEO dataset, this two‑stage pipeline achieves a balanced accuracy of 90.6%, outperforming a strong supervised baseline and demonstrating the benefit of synthetic pre‑training for underrepresented classes.

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