cff-version: 1.2.0
abstract: "<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>"
authors:
  - family-names: Ghorbani
    given-names: Ramin
    orcid: "https://orcid.org/0000-0003-3631-0177"
  - family-names: Reinders
    given-names: Marcel
    orcid: "https://orcid.org/0000-0002-1148-1562"
  - family-names: Tax
    given-names: David
    orcid: "https://orcid.org/0000-0002-5153-9087"
title: "Code underlying the publication: &#34;Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability&#34;"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/d8859c20-2569-4e3a-88b3-6c12098d8c65.v1
license: MIT
date-released: 2024-12-20