%0 Computer Program
%A Ghorbani, Ramin
%A Reinders, Marcel
%A Tax, David
%D 2024
%T Code underlying the publication: "Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability"
%U 
%R 10.4121/d8859c20-2569-4e3a-88b3-6c12098d8c65.v1
%K Self-Supervised Learning
%K Representation Learning
%K Autoencoder
%K PPG Signal
%K Human Activity Recognition
%K Inter-Subject Variability
%X <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>
%I 4TU.ResearchData