A review of deep learning methods for non-contact heart rate measurement based on facial videos.
10.7507/1001-5515.202405057
- Author:
Shuyue GUAN
1
;
Yimou LYU
1
;
Yongchun LI
2
;
Chengzhi XIA
1
;
Lin QI
1
;
Lisheng XU
1
Author Information
1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110100, P. R. China.
2. Shenyang Kanghui Brain Intelligence Collaborative Innovation Center, Shenyang 110100, P. R. China.
- Publication Type:English Abstract
- Keywords:
Deep learning;
Heart rate measurement;
Non-contact;
Remote photoplethysmography (rPPG)
- MeSH:
Humans;
Deep Learning;
Heart Rate/physiology*;
Photoplethysmography/methods*;
Video Recording;
Face;
Monitoring, Physiologic/methods*;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2025;42(1):197-204
- CountryChina
- Language:Chinese
-
Abstract:
Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
