research-article

Contactless Monitoring of PPG Using Radar

Authors:

Usman Mahmood Khan,

Muhammad ShahzadAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 6, Issue 3

Article No.: 123, Pages 1 - 30

https://doi.org/10.1145/3550330

Published: 07 September 2022 Publication History

Abstract

In this paper, we propose VitaNet, a radio frequency based contactless approach that accurately estimates the PPG signal using radar for stationary participants. The main insight behind VitaNet is that the changes in the blood volume that manifest in the PPG waveform are correlated to the physical movements of the heart, which the radar can capture. To estimate the PPG waveform, VitaNet uses a self-attention architecture to identify the most informative reflections in an unsupervised manner, and then uses an encoder decoder network to transform the radar phase profile to the PPG sequence. We have trained and extensively evaluated VitaNet on a large dataset obtained from 25 participants over 179 full nights. Our evaluations show that VitaNet accurately estimates the PPG waveform and its derivatives with high accuracy, significantly improves the heart rate and heart rate variability estimates from the prior works, and also accurately estimates several useful PPG features. We have released the codes of VitaNet as well as the trained models and the dataset used in this paper.

References

[1]

K. Takazawa et al. Assessment of vasocative agents and vascular aging by the second derivative of photoplethysmogram waveform. Hypertension, 32(2):365--370, 1998.

[2]

J. Dorlas and J. Nijboer. Photo-electric plethysmography as a monitoring device in anaesthesia. application and interpretation. British Journal of Anaesthesia, 57(5):524--530, 1985.

[3]

S. R. Alty et al. Predicting arterial stiffness from the digital volume pulse waveform. IEEE Transactions on Biomedical Engineering, 54(12):2268--2275, 2007.

[4]

I. Imanaga et al. Correlation between wave components of the second derivative of plethysmogram and arterial distensibility. Jpn Heart J, 39(6):775--784, 1998.

[5]

M. Elgendi. On the analysis of fingertip photoplethysmogram signals. Current Cardiology Reviews, 8(1):14--25, 2012.

[6]

L. Aarts et al. Non-contact heart rate monitoring utilizing camera photoplethysmography in the neonatal intensive care unit - a pilot study. Early Hum Dev, 89(12):943--948, 2013.

[7]

A. Trump et al. Camera-based photoplethysmography in an intraoperative setting. BioMedical Engineering OnLine, 17(33), 2018.

[8]

M. Kumar, A. Veeraraghavan, and A. Sabharwal. Distanceppg: Robust non-contact vital signs monitoring using a camera. Biomed Opt Express, 6(5):1565--1588, 2015.

[9]

F. Adib et al. Smart homes that monitor breathing and heart rate. In In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, page 837-846, 2016.

[10]

N. Malesevic et al. Contactless real-time heartbeat detection via 24 ghz continuous-wave doppler radar using artificial neural networks. Sensors, 20(8):350--359, 2020.

[11]

A. Rahman et al. Doppler radar techniques for accurate respiration characterization and subject identification. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 8(2):350--359, 2018.

[12]

U. Ha, S. Assana, and F. Adib. Contactless seismocardiography via deep learning radars. In MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, number 62, pages 1-14. ACM Press, 2020.

Digital Library

[13]

C. Will et al. Radar-based heart sound detection. Sci Rep, 8(11551), 2018.

[14]

J. Chen et al. Contactless electrocardiogram monitoring with millimeter wave radar. 2021.

[15]

D. Toda et al. Ecg signal reconstruction using fmcw radar and convolutional neural network. In International Symposium on Communications and Information Technologies (ISCIT), pages 176-181, 2021.

[16]

C. Will et al. Local pulse wave detection using continuous wave radar systems. 1(2):81--89, 2017.

[17]

W. Xia, Y. Li, and S.Dong. Radar-based high-accuracy cardiac activity sensing. 70:1--13, 2021.

[18]

J. Xiong and K. Jamieson. Arraytrack: A fine-grained indoor location system. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13), pages 71-84, 2013.

[19]

https://gitfront.io/r/user-1279687/e8560251380cfa75ca3ce8b77cbad826614e7654/vitanet/.

[20]

A. Hertzman. The blood supply of various skin areas as estimated by the photoelectric plethysmograph. Amer J Physiol, 124(2):329--340, 1938.

[21]

S. Millasseau et al. Determination of age-related increases in large artery stiffness by digital pulse contour analysis clinical science. Hypertension, 103(4):371--377, 2002.

[22]

Y. Aiba et al. Peripheral hemodynamics evaluated by acceleration plethysmography in workers exposed to lead. Industrial Health, 37(1):3--8, 1999.

[23]

N. Nousou et al. Classification of acceleration plethysmogram using self-organizing map. In Intelligent Signal Processing and Communications, pages 681-684, 2006.

[24]

M. Paul et al. Modeling photoplethysmographic signals in camera-based perfusion measurements: optoelectronic skin phantom. Biomed Opt Express, 10(9):4353--4368, 2019.

[25]

J. Lin et al. Design of an fmcw radar baseband signal processing system for automotive application. SpringerPlus, 2016.

[26]

M. Muller. Information Retrieval for Music and Motion, chapter: Dynamic Time Warping, pages 69-84. Springer, Berlin, Heidelberg, 2007.

[27]

B. A. Shenoi. Introduction to Digital Signal Processing and Filter Design. Wiley, Cambridge, MA, 2005.

[28]

J. C. Ye and W. K. Sung. Understanding geometry of encoder-decoder cnns. In Proceedings of the 36th International Conference on Machine Learning, pages 7064-7073, 2019.

[29]

C. Szegedy et al. Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-First AAAI Conference on Artificial Intelligence, 2017.

Digital Library

[30]

https://www.ti.com/tool/IWR6843AOPEVM.

[31]

Fadel Adib et al. Capturing the human figure through a wall. ACM Transactions on Graphics, 34(6), 2015.

[32]

https://choosemuse.com/muse-s/.

[33]

https://coral.ai/products/dev-board/.

[34]

M. Zhadobov et al. Millimeter-wave interactions with the human body: state of knowledge and recent advances. International Journal of Microwave and Wireless Technologies, 3(2):237--247, 2011.

[35]

Y. Lee et al. A novel non-contact heart rate monitor using impulse-radio ultra-wideband (ir-uwb) radar technology. Journal Clinical Monitoring and Computing, 22(1):23--29, 2008.

[36]

N. Singh et al. Heart rate variability: An old metric with new meaning in the era of using mhealth technologies for health and exercise training guidance. part one: Physiology and methods. Arrhythm Electrophysiol Rev., 7(3):193--198, 2018.

[37]

S. Lu et al. Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information? Scientific Reports, 8(13053), 2018.

[38]

L. Wang et al. Noninvasive cardiac output estimation using a novel photoplethysmogram index. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009.

[39]

M. Krzywinski and N. Altman. Significance, p values and t-tests. Nature Methods, 10:1041--1042, 2013.

Cited By

Hu QZhang QLu HWu SZhou YHuang QChen HChen YZhao N(2024)Contactless Arterial Blood Pressure Waveform Monitoring with mmWave RadarProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997818:4(1-29)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699781
Zhao LLyu RLin QZhou AZhang HMa HWang JShao CTang Y(2024)mmArrhythmiaProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435498:1(1-25)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643549
Zhang DZhang XXie YZhang FWang XLi YZhang D(2024)LoCalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314367:4(1-27)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631436
Show More Cited By

Index Terms

Contactless Monitoring of PPG Using Radar
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
    2. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Ambient intelligence
      2. Ubiquitous computing

Recommendations

Contactless seismocardiography via deep learning radars
MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking

The seismocardiogram (SCG) is a recording of a human heart's mechanical activity. It captures fine-grained cardiovascular events such as the opening and closing of heart valves and the contraction and relaxation of heart chambers. Today, SCG recordings ...
A Deep Learning Approach to Estimate SpO2 from PPG Signals
ICBRA '22: Proceedings of the 9th International Conference on Bioinformatics Research and Applications

Blood oxygen saturation level (SpO2) is one of the vital parameters determining the hemostability of a patient, besides heart rate (HR), respiratory rate (RR) and blood preasure (BP). In emergency situations with a high number of injured persons, during ...
Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

We develop an algorithm that accurately detects Atrial Fibrillation (AF) episodes from photoplethysmograms (PPG) recorded in ambulatory free-living conditions. We collect and annotate a dataset containing more than 4000 hours of PPG recorded from a ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 6, Issue 3

September 2022

1612 pages

EISSN:2474-9567

DOI:10.1145/3563014

Issue’s Table of Contents

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2022

Published in IMWUT Volume 6, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
810
Total Downloads

Downloads (Last 12 months)204
Downloads (Last 6 weeks)19

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hu QZhang QLu HWu SZhou YHuang QChen HChen YZhao N(2024)Contactless Arterial Blood Pressure Waveform Monitoring with mmWave RadarProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997818:4(1-29)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699781
Zhao LLyu RLin QZhou AZhang HMa HWang JShao CTang Y(2024)mmArrhythmiaProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435498:1(1-25)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643549
Zhang DZhang XXie YZhang FWang XLi YZhang D(2024)LoCalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314367:4(1-27)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631436
Mejdani DBräunig JGrießHammer SKrauss DSteigleder TEngel LJukic JRozhdestvenskaya AWinkler JEskofier BOstgathe CVossiek M(2024)Radar-Based Tremor Quantification Using Deep Learning for Improved Parkinson’s and Palliative Care AssessmentIEEE Transactions on Radar Systems10.1109/TRS.2024.34944732(1174-1185)Online publication date: 2024
https://doi.org/10.1109/TRS.2024.3494473
Krauss DEngel LOtt TBräunig JRicher RGambietz MAlbrecht NHille EUllmann IBraun MDabrock PKölpin AKoelewijn AEskofier BVossiek M(2024)A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical MonitoringIEEE Open Journal of Engineering in Medicine and Biology10.1109/OJEMB.2024.33972085(680-699)Online publication date: 2024
https://doi.org/10.1109/OJEMB.2024.3397208
Nocera ASenigagliesi LRaimondi MCiattaglia GGambi E(2024)Machine Learning in RADAR-Based Physiological Signals Sensing: A Scoping Review of the Models, Datasets, and MetricsIEEE Access10.1109/ACCESS.2024.348269012(156082-156117)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3482690

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents