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 - <p>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.</p>
ER -