Remote Heart Rate Estimation Based on Deep Learning

Document Type : Original Article(s)

Authors

1 PhD, Signal Processing Group, Research Center for Development of Advanced Technologies, Tehran, Iran

2 MSc, Signal Processing Group, Research Center for Development of Advanced Technologies, Tehran, Iran

10.48305/jims.v43.i836.1383

Abstract

Background: Using remote photoplethysmography (rPPG) technology, monitoring heart rate (HR) has become possible without physical contact. Over the past decades, methods have been developed to estimate rPPG signals and heart rates using video frames. Recently, deep learning techniques have also been applied in this field, showing promising performance.
Methods: In this study, a deep neural network (MTTS-CAN) along with a face detection algorithm (MediaPipe) was used to estimate heart rate remotely from videos of individuals in a public dataset (PURE) and a local dataset (Stroop).
Findings: The implementation results on the PURE dataset are comparable to those published in articles (MAE: 7.72 bpm). Moreover, the results on the local dataset are also acceptable (MAE: 5.53 bpm).
Conclusion: This paper presents an acceptable accuracy for non-contact heart rate estimation. Additionally, the results indicate that the proposed method is not dependent on a specific dataset and has produced satisfactory results in a local dataset compared to the benchmark dataset.

Highlights

Sara Pourmohammadi: PubMed, Google Scholar

Kian Shahi: Google Scholar

Keywords

Main Subjects

References

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Deniz Cyli N. rPPG based heart rate estimation using deep learning [Thesis]. Istanbul: Marmara University; 2021. 

Journal of Isfahan Medical School
Volume 43, Issue 836
4th Week, December
November and December 2026
Pages 1383-1389

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History

  • Receive Date: 09 December 2024
  • Accept Date: 21 January 2026

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