Remote heart rate measurement using low-cost RGB face video: a technical literature review

Philipp V. ROUAST, Marc T. P. ADAM, Raymond CHIONG, David CORNFORTH, Ewa LUX

PDF(2256 KB)
PDF(2256 KB)
Front. Comput. Sci. ›› 2018, Vol. 12 ›› Issue (5) : 858-872. DOI: 10.1007/s11704-016-6243-6
REVIEW ARTICLE

Remote heart rate measurement using low-cost RGB face video: a technical literature review

Author information +
History +

Abstract

Remote photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this study, we review the development of the field of rPPG since its emergence in 2008. We also classify existing rPPG approaches and derive a framework that provides an overview of modular steps. Based on this framework, practitioners can use our classification to design algorithms for an rPPG approach that suits their specific needs. Researchers can use the reviewed and classified algorithms as a starting point to improve particular features of an rPPG algorithm.

Keywords

affective computing / heart rate measurement / remote / non-contact / camera-based / photoplethysmography

Cite this article

Download citation ▾
Philipp V. ROUAST, Marc T. P. ADAM, Raymond CHIONG, David CORNFORTH, Ewa LUX. Remote heart rate measurement using low-cost RGB face video: a technical literature review. Front. Comput. Sci., 2018, 12(5): 858‒872 https://doi.org/10.1007/s11704-016-6243-6

References

[1]
Zhang Z L, Pi Z Y, Liu B Y. Troika: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Transactions on Biomedical Engineering, 2015, 62(2): 522–531
CrossRef Google scholar
[2]
Adam M T P, Krämer J, Weinhardt C. Excitement up! Price down! Measuring emotions in dutch auctions. International Journal of Electronic Commerce, 2012, 13(2): 7–39
CrossRef Google scholar
[3]
Adam M T P, Krämer J, Müller M B. Auction fever! How time pressure and social competition affect bidders’ arousal and bids in retail auctions. Journal of Retailing, 2015, 91(3): 468–485
CrossRef Google scholar
[4]
Astor P J, Adam M T P, Jeřcíc P, Schaaff K, Weinhardt C. Integrating biosignals into information systems: a neurois tool for improving emotion regulation. Journal of Management Information Systems, 2013, 30(3): 247–278
CrossRef Google scholar
[5]
Riedl R. On the biology of technostress: literature review and research agenda. ACM SIGMIS Database, 2013, 44(1): 18–55
CrossRef Google scholar
[6]
Verkruysse W, Svaasand L O, Nelson J S. Remote plethysmographic imaging using ambient light. Optics Express, 2008, 16(26): 21434–21445
CrossRef Google scholar
[7]
McDuff D J, Estepp J R, Piasecki A M, Blackford E B. A survey of remote optical photoplethysmographic imaging methods. In: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015, 6398–6404
CrossRef Google scholar
[8]
Liu H, Wang Y D, Wang L. A review of non-contact, low-cost physiological information measurement based on photoplethysmographic imaging. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012, 2088–2091
[9]
Kranjec J, Beguš S, Geršak G, Drnovšek J. Non-contact heart rate and heart rate variability measurements: a review. Biomedical Signal Processing and Control, 2014, 13(1): 102–112
CrossRef Google scholar
[10]
Poh M Z, McDuff D J, Picard R W. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express, 2010, 18(10): 10762–10774
CrossRef Google scholar
[11]
Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement, 2007, 28(3): R1–R39
CrossRef Google scholar
[12]
Lindberg L G, Öberg P A. Optical properties of blood in motion. Optical Engineering, 1993, 32(2): 253–257
CrossRef Google scholar
[13]
Balakrishnan G, Durand F, Guttag J. Detecting pulse from head motions in video. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2013, 3430–3437
CrossRef Google scholar
[14]
Starr I, Rawson A J, Schroeder H A, Joseph N R. Studies on the estimation of cardiac ouptut in man, and of abnormalities in cardiac function, from the heart’s recoil and the blood’s impacts; the ballistocardiogram. American Journal of Physiology, 1939, 127(1): 1–28
[15]
Hertzman A B, Spealman C R. Observations on the finger volume pulse recorded photoelectrically. American Journal of Physiology, 1937, 119(2): 334–335
[16]
Poh M Z, McDuff D J, Picard R W. Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Transactions on Biomedical Engineering, 2011, 58(1): 7–11
CrossRef Google scholar
[17]
Lewandowska M, Ruminski J, Kocejko T. Measuring pulse rate with a webcam: a non-contact method for evaluating cardiac activity. In: Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS). 2011, 405–410
[18]
Kwon S, Kim H, Park K S. Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012, 2174–2177
[19]
Lee K Z, Hung P C, Tsai L W. Contact-free heart rate measurement using a camera. In: Proceedings of the 9th Conference on Computer and Robot Vision. 2012, 147–152
CrossRef Google scholar
[20]
Wei L, Tian Y H, Wang Y W, Ebrahimi T, Huang T. Automatic webcam-based human heart rate measurements using laplacian eigenmap. In: Proceedings of Asian Conference on Computer Vision. 2013, 281–292
CrossRef Google scholar
[21]
Wu H Y, Rubinstein M, Shih E, Guttag J V, Durand F, Freeman W T. Eulerian video magnification for revealing subtle changes in the world. ACM Transactions on Graphics, 2012, 31(4): 1–8
CrossRef Google scholar
[22]
Wu H Y. Eulerian video processing and medical applications. Dissertation for the Master Degree. Cambridge, MA: Massachusetts Institute of Technology, 2012
[23]
Shan L, Yu M H. Video-based heart rate measurement using head motion tracking and ICA. In: Proceedings of the 6th International Congress on Image and Signal Processing. 2013, 160–164
CrossRef Google scholar
[24]
Irani R, Nasrollahi K, Moeslund T B. Improved pulse detection from head motions using DCT. In: Proceedings of the 9th International Conference on Computer Vision Theory and Applications. 2014, 118–124
[25]
De Haan G, Jeanne V. Robust pulse rate from chrominance-based rPPG. IEEE Transactions on Biomedical Engineering, 2013, 60(10): 2878–2886
CrossRef Google scholar
[26]
De Haan G, Van Leest A. Improved motion robustness of remote-PPG by using the blood volume pulse signature. Physiological Measurement, 2014, 35(9): 1913–1926
CrossRef Google scholar
[27]
Lempe G, Zaunseder S, Wirthgen T, Zipser S, Malberg H. ROI selection for remote photoplethysmography. In: Meinzer H P, Deserno M T, Handels H, et al, eds. Bildverarbeitung für die Medizin 2013. Berlin: Springer, 2013, 99–103
CrossRef Google scholar
[28]
Datcu D, Cidota M, Lukosch S, Rothkrantz L. Noncontact automatic heart rate analysis in visible spectrum by specific face regions. In: Proceedings of the 14th International Conference on Computer Systems and Technologies. 2013, 120–127
CrossRef Google scholar
[29]
Li X B, Chen J, Zhao G Y, Pietikäinen M. Remote heart rate measurement from face videos under realistic situations. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2014, 4264–4271
CrossRef Google scholar
[30]
McDuff D, Gontarek S, Picard R W. Remote detection of photoplethysmographic systolic and diastolic peaks using a digital camera. IEEE Transactions on Biomedical Engineering, 2014, 61(12): 2948–2954
CrossRef Google scholar
[31]
Kumar M, Veeraraghavan A, Sabharwal A. DistancePPG: robust noncontact vital signs monitoring using a camera. Biomedical Optics Express, 2015, 6(5): 1565–1588
CrossRef Google scholar
[32]
Tasli H E, Gudi A, Den Uyl M. Remote PPG based vital sign measurement using adaptive facial regions. In: Proceedings of IEEE International Conference on Image Processing. 2014, 1410–1414
CrossRef Google scholar
[33]
Feng L T, Po L M, Xu X Y, Li Y M. Motion artifacts suppression for remote imaging photoplethysmography. In: Proceedings of the 19th International Conference on Digital Signal Processing. 2014, 18–23
CrossRef Google scholar
[34]
Feng L T, Po L M, Xu X Y, Li Y M, Ma R Y. Motion-resistant remote imaging photoplethysmography based on the optical properties of skin. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(5): 879–891
CrossRef Google scholar
[35]
Wang W J, Stuijk S, De Haan G. Exploiting spatial redundancy of image sensor for motion robust rPPG. IEEE Transactions on Biomedical Engineering, 2015, 62(2): 415–425
CrossRef Google scholar
[36]
McDuff D, Gontarek S, Picard R W. Improvements in remote cardiopulmonary measurement using a five band digital camera. IEEE Transactions on Biomedical Engineering, 2014, 61(10): 2593–2601
CrossRef Google scholar
[37]
Chwyl B, Chung A G, Deglint J, Wong A, Claus i D A. Remote heart rate measurement through broadband video via stochastic bayesian estimation. Vision Letters, 2015, 1(1): 5
CrossRef Google scholar
[38]
Monkaresi H, Calvo R A, Yan H. A machine learning approach to improve contactless heart rate monitoring using a webcam. IEEE Journal of Biomedical and Health Informatics, 2014, 18(4): 1153–1160
CrossRef Google scholar
[39]
Hsu Y, Lin Y L, Hsu W. Learning-based heart rate detection from remote photoplethysmography features. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2014, 4433–4437
CrossRef Google scholar
[40]
Tran D N, Lee H, Kim C. A robust real time system for remote heart rate measurement via a camera. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2015, 1–6
[41]
Li M C, Lin Y H. A real-time non-contact pulse rate detector based on smartphone. In: Proceedings of International Symposium on Next- Generation Electronics. 2015, 1–3
CrossRef Google scholar
[42]
Yu Y P, Kwan B H, Lim C L, Wong S L, Raveendran P. Video-based heart rate measurement using short-time fourier transform. In: Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems. 2013, 704–707
CrossRef Google scholar
[43]
Holton B D, Mannapperuma K, Lesniewski P J, Thomas J C. Signal recovery in imaging photoplethysmography. Physiological Measurement, 2013, 34(11): 1499–1511
CrossRef Google scholar
[44]
Xu S C, Sun L Y, Rohde G K. Robust efficient estimation of heart rate pulse from video. Biomedical Optics Express, 2014, 5(4): 1124–1135
CrossRef Google scholar
[45]
Feng L T, Po L M, Xu X Y, Li Y M, Cheung C-H, Cheung K-W, Yuan F. Dynamic ROI based on K-means for remote photoplethysmography. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2015, 1310–1314
CrossRef Google scholar
[46]
Han B, Ivanov K, Wang L, Yan Y. Exploration of the optimal skincamera distance for facial photoplethysmographic imaging measurement using cameras of different types. In: Proceedings of the 5th EAI International Conference onWireless Mobile Communication and Healthcare. 2015, 186–189
[47]
Zhang K H, Zhang L, Yang M H. Real-time compressive tracking. In: Proceedings of European Conference on Computer Vision. 2012, 864–877
CrossRef Google scholar
[48]
Fernández A, Carúz J L, Usamentiaga R, Alvarez E, Casado R. Unobtrusive health monitoring system using video-based physiological information and activity measurements. In: Proceedings of International Conference on Computer, Information and Telecommunication Systems. 2012, 1–5
[49]
Danelljan M, Häger G, Felsberg M. Accurate scale estimation for robust visual tracking. In: Proceedings of the British Machine Vision Conference. 2014, 1–10
CrossRef Google scholar
[50]
Hoffmann K P. Biosignale erfassen und verarbeiten. In: Kramme R, ed. Medizintechnik. Berlin: Springer, 2011, 667–688
CrossRef Google scholar
[51]
Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001, 511–518
CrossRef Google scholar
[52]
Cootes T F, Edwards G J, Taylor C J. Active appearance models. In: Proceedings of European Conference on Computer Vision. 1998, 484–498
CrossRef Google scholar
[53]
Asthana A, Zafeiriou S, Cheng S Y, Pantic M. Robust discriminative response map fitting with constrained local models. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2013, 3444–3451
CrossRef Google scholar
[54]
Saragih J M, Lucey S, Cohn J F. Deformable model fitting by regularized landmark mean-shift. International Journal of Computer Vision, 2011, 91(2): 200–215
CrossRef Google scholar
[55]
Martinez B, Valstar M F, Binefa X, Pantic M. Local evidence aggregation for regression-based facial point detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(5): 1149–1163
CrossRef Google scholar
[56]
Shi J B, Tomasi C. Good features to track. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1994, 593–600
[57]
Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence. 1981, 674–679
[58]
Bay H, Tuytelaars T, Van Gool L. SURF: speeded up robust features. In: Proceedings of European Conference on Computer Vision. 2006, 404–417
CrossRef Google scholar
[59]
Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564–577
CrossRef Google scholar
[60]
Henriques J F, Caseiro R, Martins P, Batista J. Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of European Conference on Computer Vision. 2012, 702–715
CrossRef Google scholar
[61]
Farnebäck G. Two-frame motion estimation based on polynomial expansion. In: Proceedings of Scandinavian Conference on Image Analysis. 2003, 363–370
CrossRef Google scholar
[62]
Tarvainen M P, Ranta-Aho P O, Karjalainen P A. An advanced detrending method with application to HRV analysis. IEEE Transactions on Biomedical Engineering, 2002, 49(2): 172–175
CrossRef Google scholar
[63]
Rouast P V, Adam M T P, Cornforth D J, Lux E, Weinhardt C. Using contactless heart rate measurements for real-time assessment of affective states. In: Davis F D, Riedl R, Vom Brocke J, et al, eds. Information Systems and Neuroscience. Springer International Publishing, 2016, 157–163
[64]
Monkaresi H, Hussain M S, Calvo R A. Using remote heart rate measurement for affect detection. In: Proceedings of the 27th International Florida Artificial Intelligence Research Society Conference. 2014, 118–123
[65]
Zhao F, Li M, Qian Y, Tsien J Z. Remote measurements of heart and respiration rates for telemedicine. PLoS ONE, 2013, 8(10): e71384
CrossRef Google scholar
[66]
McDuff D, Gontarek S, Picard R. Remote measurement of cognitive stress via heart rate variability. In: Proceedings of the 36th IEEE Annual International Conference of Engineering in Medicine and Biology Society. 2014, 2957–2960
CrossRef Google scholar
[67]
Rahman M A, Barai A, Islam M A, Hashem M M A. Development of a device for remote monitoring of heart rate and body temperature. In: Proceedings of the 15th International Conference on Computer and Information Technology. 2012, 411–416
CrossRef Google scholar

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
AI Summary AI Mindmap
PDF(2256 KB)

Accesses

Citations

Detail

Sections
Recommended

/