Effectiveness of consumer-grade contactless vital signs monitors: a systematic review and meta-analysis

Chi Pham, Khashayar Poorzargar, Mahesh Nagappa, Aparna Saripella, Matteo Parotto, Marina Englesakis, Kang Lee, Frances Chung, Chi Pham, Khashayar Poorzargar, Mahesh Nagappa, Aparna Saripella, Matteo Parotto, Marina Englesakis, Kang Lee, Frances Chung

Abstract

The objective of this systematic review and meta-analysis was to analyze the effectiveness of contactless vital sign monitors that utilize a consumer-friendly camera versus medical grade instruments. A multiple database search was conducted from inception to September 2020. Inclusion criteria were as follows: studies that used a consumer-grade camera (smartphone/webcam) to examine contactless vital signs in adults; evaluated the non-contact device against a reference medical device; and used the participants' face for measurement. Twenty-six studies were included in the review of which 16 were included in Pearson's correlation and 14 studies were included in the Bland-Altman meta-analysis. Twenty-two studies measured heart rate (HR) (92%), three measured blood pressure (BP) (12%), and respiratory rate (RR) (12%). No study examined blood oxygen saturation (SpO2). Most studies had a small sample size (≤ 30 participants) and were performed in a laboratory setting. Our meta-analysis found that consumer-grade contactless vital sign monitors were accurate in comparison to a medical device in measuring HR. Current contactless monitors have limitations such as motion, poor lighting, and lack of automatic face tracking. Currently available consumer-friendly contactless monitors measure HR accurately compared to standard medical devices. More studies are needed to assess the accuracy of contactless BP and RR monitors. Implementation of contactless vital sign monitors for clinical use will require validation in a larger population, in a clinical setting, and expanded to encompass other vital signs including BP, RR, and SpO2.

Keywords: Blood pressure; Camera; Contactless monitors; Heart rate; Photoplethysmography; Vital signs.

Conflict of interest statement

Frances Chung: Reports research support from University Health Network Foundation, Up-to-date royalties, consultant to Takeda Pharma, STOP-Bang proprietary to University Health Network. All other authors have no conflicts to disclose.

© 2021. The Author(s), under exclusive licence to Springer Nature B.V.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram. Flow diagram of the study selection process
Fig. 2
Fig. 2
Forest plot of Pearson’s correlation coefficients of heart rate measurements at rest in contactless vital signs monitor compared to a reference medical device. Forest plot of random-effects meta-analysis on Pearson’s values showing pooled weighted correlation coefficient of 0.962 (95% CI 0.905 to 0.985; p value 2) is 93%, while between-study variance is (Tau2) 0.873 (Tau = 0.934)
Fig. 3
Fig. 3
Forest plot of standard error of the mean of heart rate measurements at rest in contactless vital signs monitor compared to a reference medical device. Forest plot of pooled estimate of the mean difference between non-contact and standard method for HR detection was 0.36, with the pooled 95% confidence interval ranging from − 1.22 to 1.95

References

    1. Hollander JE, Carr BG. Virtually perfect? Telemedicine for Covid-19. N Engl J Med. 2020;382(18):1679–1681. doi: 10.1056/NEJMp2003539.
    1. Wosik J, Fudim M, Cameron B, Gellad ZF, Cho A, Phinney D, et al. Telehealth transformation: COVID-19 and the rise of virtual care. J Am Med Inform Assoc. 2020;27(6):957–962. doi: 10.1093/jamia/ocaa067.
    1. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. doi: 10.1371/journal.pmed.1000097.
    1. Kottner J, Audigé L, Brorson S, Donner A, Gajewski BJ, Hróbjartsson A, et al. Guidelines for reporting reliability and agreement studies (GRRAS) were proposed. Int J Nurs Stud. 2011;48(6):661–671. doi: 10.1016/j.ijnurstu.2011.01.016.
    1. Harford M, Catherall J, Gerry S, Young JD, Watkinson P. Availability and performance of image-based, non-contact methods of monitoring heart rate, blood pressure, respiratory rate, and oxygen saturation: a systematic review. Physiol Meas. 2019;40(6):06. doi: 10.1088/1361-6579/ab1f1d.
    1. Bland JM, Altman DG. Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat. 2007;17(4):571–582. doi: 10.1080/10543400701329422.
    1. Hunter JE, Schmidt FL. Methods of meta-analysis: correcting error and bias in research findings. Sage; 2004.
    1. Tipton E, Shuster J. A framework for the meta-analysis of Bland-Altman studies based on a limits of agreement approach. Stat Med. 2017;36(23):3621–3635. doi: 10.1002/sim.7352.
    1. Yang D, Xiao G, Wei J, Luo H. Preliminary assessment of video-based blood pressure measurement according to ANSI/AAMI/ISO81060-2: 2013 guideline accuracy criteria: anura smartphone app with transdermal optimal imaging technology. Blood Press Monit. 2020;25(5):295–298. doi: 10.1097/MBP.0000000000000467.
    1. Wang D, Yang X, Liu X, Jing J, Fang S. Detail-preserving pulse wave extraction from facial videos using consume-level camera. Biomed Opt Express. 2020;11(4):1876–1891. doi: 10.1364/BOE.380646.
    1. Benedetto S, Caldato C, Greenwood DC, Bartoli N, Pensabene V, Actis P. Remote heart rate monitoring-assessment of the facereader rPPg by Noldus. PLoS ONE. 2019;14(11):0225592. doi: 10.1371/journal.pone.0225592.
    1. Luo H, Yang D, Barszczyk A, Vempala N, Wei J, Wu SJ, et al. Smartphone-based blood pressure measurement using transdermal optical imaging technology. Circ Cardiovas Imaging. 2019;12(8):008857. doi: 10.1161/CIRCIMAGING.119.008857.
    1. Bousefsaf F, Maaoui C, Pruski A. Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate. Biomed Signal Process Control. 2013;8(6):568–574. doi: 10.1016/j.bspc.2013.05.010.
    1. Couderc J-P, Kyal S, Mestha LK, Xu B, Peterson DR, Xia X, et al. Detection of atrial fibrillation using contactless facial video monitoring. Heart Rhythm. 2015;12(1):195–201. doi: 10.1016/j.hrthm.2014.08.035.
    1. Holton BD, Mannapperuma K, Lesniewski PJ, Thomas JC. Signal recovery in imaging photoplethysmography. Physiol Meas. 2013;34(11):1499. doi: 10.1088/0967-3334/34/11/1499.
    1. Lewandowska M, Nowak J. Measuring pulse rate with a webcam. J Med Imaging Health Informat. 2012;2(1):87–92. doi: 10.1166/jmihi.2012.1064.
    1. Poh M-Z, McDuff DJ, Picard RW. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt Express. 2010;18(10):10762–10774. doi: 10.1364/OE.18.010762.
    1. Poh M-Z, McDuff DJ, Picard RW. Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans Biomed Eng. 2010;58(1):7–11. doi: 10.1109/TBME.2010.2086456.
    1. Rodríguez AM, Ramos-Castro J. Video pulse rate variability analysis in stationary and motion conditions. Biomed Eng Online. 2018;17(1):1–26. doi: 10.1186/s12938-017-0432-x.
    1. Sanyal S, Nundy KK. Algorithms for monitoring heart rate and respiratory rate from the video of a user’s face. IEEE Journal of translational engineering in health and medicine. 2018;6:1–11. doi: 10.1109/JTEHM.2018.2818687.
    1. Tang C, Lu J, Liu J Non-contact heart rate monitoring by combining convolutional neural network skin detection and remote photoplethysmography via a low-cost camera. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops2018. p. 1309–15.
    1. Tsouri GR, Kyal S, Dianat SA, Mestha LK. Constrained independent component analysis approach to nonobtrusive pulse rate measurements. J Biomed Optics. 2012;17(7):077011. doi: 10.1117/1.JBO.17.7.077011.
    1. Xu S, Sun L, Rohde GK. Robust efficient estimation of heart rate pulse from video. Biomed Opt Express. 2014;5(4):1124–1135. doi: 10.1364/BOE.5.001124.
    1. Yan BP, Chan CK, Li CK, To OT, Lai WH, Tse G, et al. Resting and postexercise heart rate detection from fingertip and facial photoplethysmography using a smartphone camera: a validation study. JMIR Mhealth Uhealth. 2017;5(3):e33. doi: 10.2196/mhealth.7275.
    1. Zhu H, Zhao Y, Dong L. Non-contact detection of cardiac rate based on visible light imaging device. Optics and Photonics for Information Processing VI: International Society for Optics and Photonics; 2012. p. 849806.
    1. Coppetti T, Brauchlin A, Müggler S, Attinger-Toller A, Templin C, Schönrath F, et al. Accuracy of smartphone apps for heart rate measurement. Eur J Prev Cardiol. 2017;24(12):1287–1293. doi: 10.1177/2047487317702044.
    1. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14(1):1–13. doi: 10.1186/1471-2288-14-135.
    1. Huang R-Y, Dung L-R. Measurement of heart rate variability using off-the-shelf smart phones. Biomed Eng Online. 2016;15(1):1–16. doi: 10.1186/s12938-015-0119-0.
    1. Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR. Non-contact heart rate and blood pressure estimations from video analysis and machine learning modelling applied to food sensory responses: A case study for chocolate. Sensors. 2018;18(6):1802. doi: 10.3390/s18061802.
    1. Sun Y, Azorin-Peris V, Kalawsky R, Hu S, Papin C, Greenwald SE. Use of ambient light in remote photoplethysmographic systems: comparison between a high-performance camera and a low-cost webcam. J Biomed Optics. 2012;17(3):037005. doi: 10.1117/1.JBO.17.3.037005.
    1. Li X, Chen J, Zhao G, Pietikainen M (2014) Remote heart rate measurement from face videos under realistic situations. Proceedings of the IEEE conference on computer vision and pattern recognition 4264–71.
    1. Wei B, He X, Zhang C, Wu X. Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions. Biomed Eng Online. 2017;16(1):1–21. doi: 10.1186/s12938-016-0292-9.
    1. Koprowski R. Blood pulsation measurement using cameras operating in visible light: limitations. Biomed Eng Online. 2016;15(1):1–15. doi: 10.1186/s12938-015-0119-0.
    1. Lin J, Rozado D, Duenser A. Improving video based heart rate monitoring. Stud Health Technol Inform. 2015;214:146–151.
    1. Cheatham SW, Kolber MJ, Ernst MP. Concurrent validity of resting pulse-rate measurements: a comparison of 2 smartphone applications, the polar H7 belt monitor, and a pulse oximeter with bluetooth. J Sport Rehabil. 2015;24(2):171–178. doi: 10.1123/jsr.2013-0145.
    1. Humphreys K, Ward T, Markham C. Noncontact simultaneous dual wavelength photoplethysmography a further step toward noncontact pulse oximetry. Rev Sci Ins. 2007;78(4):044304. doi: 10.1063/1.2724789.
    1. Wieringa FP, Mastik F, van der Steen AF. Contactless multiple wavelength photoplethysmographic imaging: A first step toward “SpO 2 camera” technology. Ann Biomed Eng. 2005;33(8):1034–1041. doi: 10.1007/s10439-005-5763-2.
    1. Jubran A. Pulse oximetry. Intensive Care Med. 2004;30(11):2017–2020. doi: 10.1007/s00134-004-2399-x.
    1. Kamshilin AA, Nippolainen E, Sidorov IS, Vasilev PV, Erofeev NP, Podolian NP, et al. A new look at the essence of the imaging photoplethysmography. Sci Rep. 2015;5(1):1–9. doi: 10.1038/srep10494.
    1. Fallow BA, Tarumi T, Tanaka H. Influence of skin type and wavelength on light wave reflectance. J Clin Monit Comput. 2013;27(3):313–317. doi: 10.1007/s10877-013-9436-7.
    1. Abbo AR, Miller A, Gazit T, Savir Y, Caspi O. Technological Developments and strategic management for overcoming the COVID-19 challenge within the hospital setting in Israel. Rambam Maimonides Med J. 2020;11(3):0026. doi: 10.5041/RMMJ.10417.
    1. Bokolo AJ. Exploring the adoption of telemedicine and virtual software for care of outpatients during and after COVID-19 pandemic. Ir J Med Sci. 2021;190(1):1–10. doi: 10.1007/s11845-020-02299-z.
    1. Zaki R, Bulgiba A, Ismail R, Ismail NA. Statistical methods used to test for agreement of medical instruments measuring continuous variables in method comparison studies: a systematic review. PLoS ONE. 2012;7(5):e37908. doi: 10.1371/journal.pone.0037908.
    1. Clarke WL, Cox D, Gonder-Frederick LA, Carter W, Pohl SL. Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care. 1987;10(5):622–8. doi: 10.2337/diacare.10.5.622.
    1. Breteler MJM, KleinJan EJ, Dohmen DAJ, Leenen LPH, van Hillegersberg R, Ruurda JP, et al. Vital signs monitoring with wearable sensors in high-risk surgical patients: a clinical validation study. Anesthesiology. 2020;132(3):424–39. doi: 10.1097/aln.0000000000003029.
    1. Breteler MJMM, Huizinga E, van Loon K, Leenen LPH, Dohmen DAJ, Kalkman CJ, et al. Reliability of wireless monitoring using a wearable patch sensor in high-risk surgical patients at a step-down unit in the Netherlands: a clinical validation study. BMJ Open. 2018;8(2):e020162. doi: 10.1136/bmjopen-2017-020162.
    1. Van Stralen K, Dekker F, Zoccali C, Jager K. Measuring agreement, more complicated than it seems. Nephron Clin Pract. 2012;120(3):c162–7. doi: 10.1159/000337798.
    1. Antink CH, Lyra S, Paul M, Yu X, Leonhardt S. A broader look: camera-based vital sign estimation across the spectrum. Yearb Med Inform. 2019;28(1):102. doi: 10.1055/s-0039-1677914.

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