Non-contact heart rate detection via periodic signal detection methods

Gizem Tabak; Andrew C. Singer

doi:10.1109/ACSSC.2015.7421242

Non-contact heart rate detection via periodic signal detection methods

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Real time non-contact heart rate detection is performed by using different methods for periodic signal detection in noise. Heart rate is estimated from a scalar signal formed from the average color values of the observed skin from videos recorded previously at first, and then captured in real time with a built-in laptop webcam. Cramer-Rao lower bounds are calculated as a function of SNR for an assumed parametric model, and heart rate is detected via autocorrelation, maximum likelihood and Fourier-based methods. The performance is evaluated by comparing the error rates for different detection techniques in each case.

Original language	English (US)
Title of host publication	Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	790-794
Number of pages	5
ISBN (Electronic)	9781467385763
DOIs	https://doi.org/10.1109/ACSSC.2015.7421242
State	Published - Feb 26 2016
Event	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2016-February
ISSN (Print)	1058-6393

Other

Other	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/Territory	United States
City	Pacific Grove
Period	11/8/15 → 11/11/15

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Online availability

10.1109/ACSSC.2015.7421242

Library availability

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Cite this

Tabak, G., & Singer, A. C. (2016). Non-contact heart rate detection via periodic signal detection methods. In M. B. Matthews (Ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 (pp. 790-794). [7421242] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421242

Non-contact heart rate detection via periodic signal detection methods. / Tabak, Gizem; Singer, Andrew C.

Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. ed. / Michael B. Matthews. IEEE Computer Society, 2016. p. 790-794 7421242 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tabak, G & Singer, AC 2016, Non-contact heart rate detection via periodic signal detection methods. in MB Matthews (ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015., 7421242, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2016-February, IEEE Computer Society, pp. 790-794, 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 11/8/15. https://doi.org/10.1109/ACSSC.2015.7421242

Tabak G, Singer AC. Non-contact heart rate detection via periodic signal detection methods. In Matthews MB, editor, Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. IEEE Computer Society. 2016. p. 790-794. 7421242. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2015.7421242

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abstract = "Real time non-contact heart rate detection is performed by using different methods for periodic signal detection in noise. Heart rate is estimated from a scalar signal formed from the average color values of the observed skin from videos recorded previously at first, and then captured in real time with a built-in laptop webcam. Cramer-Rao lower bounds are calculated as a function of SNR for an assumed parametric model, and heart rate is detected via autocorrelation, maximum likelihood and Fourier-based methods. The performance is evaluated by comparing the error rates for different detection techniques in each case.",

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AB - Real time non-contact heart rate detection is performed by using different methods for periodic signal detection in noise. Heart rate is estimated from a scalar signal formed from the average color values of the observed skin from videos recorded previously at first, and then captured in real time with a built-in laptop webcam. Cramer-Rao lower bounds are calculated as a function of SNR for an assumed parametric model, and heart rate is detected via autocorrelation, maximum likelihood and Fourier-based methods. The performance is evaluated by comparing the error rates for different detection techniques in each case.

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Non-contact heart rate detection via periodic signal detection methods

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