ATHLAB dataset

Advancements in machine learning, particularly in architectures such as feedforward neural networks (FNN), graph neural networks (GNN), and transformers, offer new opportunities for modeling complex physiological signals. This Dataset is foundation basis for study investigates the application of these models to individualized optoelectronic plethysmography (OEP) data, intending to enable more personalized respiratory health assessments and performance interventions.By embedding OEP data into a structured modeling pipeline and leveraging temporal attention mechanisms, we aim to uncover latent patterns linking breathing behavior and physical performance. The outcome is a foundation for athlete-specific modeling that can generalize to broader health, recovery, and optimization frameworks.