{rfName}
KI

Indexed in

License and use

Citations

1

Altmetrics

Article has an altmetric score of 1

Analysis of institutional authors

Miranda J.Author

Share

February 27, 2025
Publications
>
Article
Green

KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch

Publicated to:Ieee Transactions On Biomedical Engineering. - 2024-01-01 (), DOI: 10.1109/TBME.2024.3477275

Authors: Kechris C; Dan J; Miranda J; Atienza D

Affiliations

Epfl; Embedded Systems Laboratory; Switzerland - Author

Abstract

Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they often underutilize existing knowledge from the medical and signal processing community. In this paper, we address three shortcomings of deep learning models: motion artifact removal, degradation assessment, and physiologically plausible analysis of the PPG signal. We propose KID-PPG, a knowledge-informed deep learning model that integrates expert knowledge through adaptive linear filtering, deep probabilistic inference, and data augmentation. We evaluate KID-PPG on the PPGDalia dataset, achieving an average mean absolute error of 2.85 beats per minute, surpassing existing reproducible methods. Our results demonstrate a significant performance improvement in heart rate tracking through the incorporation of prior knowledge into deep learning models. This approach shows promise in enhancing various biomedical applications by incorporating existing expert knowledge in deep learning models. © 1964-2012 IEEE.

Keywords

AccelerationAdaptive filteringAdaptive filtersArticleArtifactDeep learningExpert knowledgeFiltrationHeartHeart rateHeart-rateHumanKnowledge informed aiLearning methodsLearning modelsMean absolute errorMotionMotion artifactMotion artifactsMotion signalsPhotoelectric plethysmographyPhotoplethysmograhyPhotoplethysmographyPhysiological modelsProbabilistic reasoningSignal degradationSignal processingSource separation

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Ieee Transactions On Biomedical Engineering due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Biomedical Engineering.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-11:

  • Scopus: 1

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-11:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 22.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 24 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 3 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Switzerland.