A Comparative Study of Common Steps in Video-based Remote Heart Rate Detection Methods

Malasinghe, Lakmini; Katsigiannis, Stamos; Dahal, Keshav; Ramzan, Naeem

doi:10.1016/j.eswa.2022.117867

A Comparative Study of Common Steps in Video-based Remote Heart Rate Detection Methods

Malasinghe, Lakmini; Katsigiannis, Stamos; Dahal, Keshav; Ramzan, Naeem

Authors

Lakmini Malasinghe

Dr Stamos Katsigiannis stamos.katsigiannis@durham.ac.uk
Assistant Professor

Keshav Dahal

Naeem Ramzan

Abstract

Video-based remote heart rate detection is a promising technology that can offer convenient and low-cost heart rate monitoring within, but not limited to, the clinical environment, especially when attaching electrodes or pulse oximeters on a person is not possible or convenient. In this work, we examined common steps used in video-based remote heart rate detection algorithms, in order to evaluate their effect on the overall performance of the remote heart rate detection pipeline. Various parameters of the examined methods were evaluated on three public and one proprietary dataset in order to establish a video-based remote heart rate detection pipeline that provides the most balanced performance across various diverse datasets. The experimental evaluation demonstrated the effect and contribution of each step and parameter set on the estimation of the heart rate, resulting in an optimal configuration that achieved a best RMSE value of 9.51.

Citation

Malasinghe, L., Katsigiannis, S., Dahal, K., & Ramzan, N. (2022). A Comparative Study of Common Steps in Video-based Remote Heart Rate Detection Methods. Expert Systems with Applications, 207, Article 117867. https://doi.org/10.1016/j.eswa.2022.117867

Journal Article Type	Article
Acceptance Date	Jun 11, 2022
Online Publication Date	Jun 30, 2022
Publication Date	Nov 30, 2022
Deposit Date	Jun 13, 2022
Publicly Available Date	Jul 1, 2022
Journal	Expert Systems with Applications
Print ISSN	0957-4174
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	207
Article Number	117867
DOI	https://doi.org/10.1016/j.eswa.2022.117867

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http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright Statement
© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).