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基于深度学习的无接触心率测量方法综述。

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods.

机构信息

Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.

出版信息

Sensors (Basel). 2021 May 27;21(11):3719. doi: 10.3390/s21113719.

DOI:10.3390/s21113719
PMID:34071736
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8198867/
Abstract

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

摘要

在医疗保健和运动应用中，人们对非接触式或远程心率测量的兴趣一直在稳步增长。非接触式方法涉及使用摄像机和图像处理算法。最近，深度学习方法已被用于提高传统非接触式心率测量方法的性能。在提供相关文献综述后，本文对可公开获取代码的深度学习方法进行了比较。UBFC 公共数据集用于比较这些深度学习方法在心率测量方面的性能。结果表明，在这些方法中，PhysNet 深度学习方法的心率测量结果最佳，平均绝对误差值为 2.57 次/分钟，均方误差值为 7.56 次/分钟。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/b427a8f9fa79/sensors-21-03719-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/fecfd65ae28a/sensors-21-03719-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/d10abca8e906/sensors-21-03719-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/98a792683909/sensors-21-03719-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/8a3e903edf06/sensors-21-03719-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/5175b7f31a21/sensors-21-03719-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/63e4f92270cd/sensors-21-03719-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/89ef1b706445/sensors-21-03719-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/6c122693ded2/sensors-21-03719-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/7a9191a0b803/sensors-21-03719-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/d85cc1b7f9a8/sensors-21-03719-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/d7f963640225/sensors-21-03719-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/95ac9768c38e/sensors-21-03719-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/952bd50abcf0/sensors-21-03719-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/f335220987f5/sensors-21-03719-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/b427a8f9fa79/sensors-21-03719-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/fecfd65ae28a/sensors-21-03719-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/d10abca8e906/sensors-21-03719-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/98a792683909/sensors-21-03719-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/8a3e903edf06/sensors-21-03719-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/5175b7f31a21/sensors-21-03719-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/63e4f92270cd/sensors-21-03719-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/89ef1b706445/sensors-21-03719-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/6c122693ded2/sensors-21-03719-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/7a9191a0b803/sensors-21-03719-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/d85cc1b7f9a8/sensors-21-03719-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/d7f963640225/sensors-21-03719-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/95ac9768c38e/sensors-21-03719-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/952bd50abcf0/sensors-21-03719-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/f335220987f5/sensors-21-03719-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64b3/8198867/b427a8f9fa79/sensors-21-03719-g015.jpg

相似文献

[1]
A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods.

Sensors (Basel). 2021-5-27

[2]
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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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Front Bioeng Biotechnol. 2024-7-17

[9]
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[10]
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引用本文的文献

[1]
Diffusion-Phys: noise-robust heart rate estimation from facial videos via diffusion models.

Biomed Eng Lett. 2025-4-9

[2]
Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed.

Sensors (Basel). 2024-3-15

[3]
Capturing the pulse: a state-of-the-art review on camera-based jugular vein assessment.

Biomed Opt Express. 2023-11-28

[4]
A System for Monitoring Animals Based on Behavioral Information and Internal State Information.

Animals (Basel). 2024-1-16

[5]
Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites.

Sensors (Basel). 2023-7-17

[6]
Photoplethysmography upon cold stress-impact of measurement site and acquisition mode.

Front Physiol. 2023-6-1

[7]
Accuracy of Self-Injection Locking Radar System for Vital Signs Detection During the COVID-19 Pandemic at a Hospital in Taiwan: Measuring Vital Signs Accurately with SIL Radar for Hospital Healthcare.

Med Sci Monit. 2023-5-15

[8]
Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware.

Sensors (Basel). 2023-5-7

[9]
An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation.

Sensors (Basel). 2023-3-23

[10]
A novel contact-free atrial fibrillation monitor: a pilot study.

Eur Heart J Digit Health. 2021-12-31

本文引用的文献

[1]
Biometric Signals Estimation Using Single Photon Camera and Deep Learning.

Sensors (Basel). 2020-10-27

[2]
Assessing the Quality of Heart Rate Variability Estimated from Wrist and Finger PPG: A Novel Approach Based on Cross-Mapping Method.

Sensors (Basel). 2020-6-2

[3]
Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG.

Physiol Meas. 2020-5-7

[4]
Analysis of CNN-based remote-PPG to understand limitations and sensitivities.

Biomed Opt Express. 2020-2-7

[5]
iPhys: An Open Non-Contact Imaging-Based Physiological Measurement Toolbox.

Annu Int Conf IEEE Eng Med Biol Soc. 2019-7

[6]
Photoplethysmography based atrial fibrillation detection: a review.

NPJ Digit Med. 2020-1-10

[7]
Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit.

NPJ Digit Med. 2019-12-12

[8]
Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks.

Sensors (Basel). 2019-7-12

[9]
Heart Rate Monitoring in Newborn Babies: A Systematic Review.

Neonatology. 2019-6-27

[10]
A review on wearable photoplethysmography sensors and their potential future applications in health care.

Int J Biosens Bioelectron. 2018

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