TY - DATA T1 - Code underlying the publication: "Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability" PY - 2024/12/20 AU - Ramin Ghorbani AU - Marcel Reinders AU - David Tax UR - DO - 10.4121/d8859c20-2569-4e3a-88b3-6c12098d8c65.v1 KW - Self-Supervised Learning KW - Representation Learning KW - Autoencoder KW - PPG Signal KW - Human Activity Recognition KW - Inter-Subject Variability N2 -

This repository provides the implementation of a Self-Supervised Learning (SSL) framework for photoplethysmography (PPG) signal representation, as detailed in the paper "Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability." The framework addresses label scarcity in PPG data analysis by utilizing signal reconstruction as a pretext task to learn informative representations, with a focus on applications such as activity recognition. The study highlights that, while SSL improves downstream supervised task performance and enables the use of simpler models, significant inter-subject variability remains a challenge, limiting the model’s generalization capabilities.

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