Heart Rate Estimation from Noisy PPGs Using 1D/2D Conversion and Transfer Learning
Authors: 
Emil Dark, Umer Saleem, Arttu Lämsä, Constantino Álvarez Casado, Miguel Bordallo LópezAuthors Info & Claims
Pages 163 - 167
Abstract
In this article, we introduce a new approach for estimating the heart rate from noisy photoplethysmography (PPG) signals. We propose the use of two-dimensional representations of signals that are fed into a residual deep neural network that performs the regression task. Our approach leverages transfer learning and pre-trained models to further reduce the prediction error, resulting in state-of-the-art results in a challenging benchmark dataset.
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Index Terms
- Heart Rate Estimation from Noisy PPGs Using 1D/2D Conversion and Transfer Learning
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Published In
September 2022
538 pages
ISBN:9781450394239
DOI:10.1145/3544793
Copyright © 2022 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 April 2023
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- Research-article
- Research
- Refereed limited
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- the Academy of Finland 6G Flagship program
- ECSEL Joint Undertaking (JU) - InSecTT
- PROFI5 HiDyn
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UbiComp/ISWC '22
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UbiComp/ISWC '22: The 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing
September 11 - 15, 2022
Cambridge, United Kingdom
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Overall Acceptance Rate 764 of 2,912 submissions, 26%
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