Skip to main content
SpringerLink
Log in

Analysis of vital signs using remote photoplethysmography (RPPG)

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

In health care applications, an evolution of electronics has made drastic advancements. There are some problems created due to this advancement. To estimate the coronary heart rate, till date some problems have been confronted. To overcome these issues, remote photoplethysmography (RPPG) technology is used to determine the heart rate (HR) and respiratory rate (RR) by using normal web cameras, without any additional hardware. Here, a high resolution camera detects the face using a face detector by means of image processing techniques. Hardware part is only used to display the heart rate and respiratory rate using sensors. The performance analysis demonstrates the practicality of the patients. Experimental results of heart rate measurement show that the proposed dynamic ROI method for RIPPG can effectively improve the RIPPG signal quality, compared with the state-of-the-art ROI methods for RIPPG. Objective performance tests show strong correlation with the ground truth values for the estimated heart rate and variation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Alafeef M, Fraiwan M (2020) Smartphone-based respiratory rate estimation using photoplethysmographic imaging and discrete wavelet transform. J Ambient Intell Hum Comput 11:693–703. https://doi.org/10.1007/s12652-019-01339-6

    Article  Google Scholar 

  • Alhammad SA (2018) Face detection for pulse rate measurement. In: 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), pp. 1–5. IEEE

  • Allen J (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 28:R1–R39

    Article  Google Scholar 

  • Alsulami MH, Almuayqil SN, Atkins AS (2021) A comparison between heart-rate monitoring smart devices for ambient assisted living. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-021-03025-y

    Article  Google Scholar 

  • Brown T, Beightol L, Koh J, Eckberg D (1993) Important influence of respiration on human RR interval power spectra is largely ignored. J Appl Physiol 75:2310–2317

    Article  Google Scholar 

  • Carvalho L, Virani HG, Kutty S (2014) Analysis of heart rate monitoring using a webcam. Int J Adv Res Comput Commun Eng 3:6593–6595

    Google Scholar 

  • De Haan G, Van Leest A (2014) Improved motion robustness of remote-PPG by using the blood volume pulse signature. Physiol Meas 35(9):1913

    Article  Google Scholar 

  • Dubey H, Kumaresan R, Mankodiya K (2018) Harmonic sum-based method for heart rate estimation using PPG signals affected with motion artifacts. J Ambient Intell Hum Comput 9:137–150. https://doi.org/10.1007/s12652-016-0422-z

    Article  Google Scholar 

  • El Attaoui A, Largo S, Jilbab A et al (2020) Wireless medical sensor network for blood pressure monitoring based on machine learning for real-time data classification. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-02660-1

    Article  Google Scholar 

  • Emrah Talsi H, Gudi A, den Uyl M (2014) Remote PPG based vital sign measurement using adaptive facial regions vicarious perception technologies. Intelligent Systems Lab Amsterdam, University of Amsterdam, The Netherlands

    Google Scholar 

  • Fan X, Ye Q, Yang X et al (2020) Robust blood pressure estimation using an RGB camera. J Ambient Intell Hum Comput 11:4329–4336. https://doi.org/10.1007/s12652-018-1026-6

    Article  Google Scholar 

  • Feng L, Po LM, Xu X, Li Y, Ma R (2014) Motion-resistant remote imaging photoplethysmography based on the optical properties of skin. IEEE Trans Circuits Syst Video Technol 25(5):879–891

    Article  Google Scholar 

  • Gil E, Orini M, Bailon R, Vergara J, Mainardi L, Laguna P (2010) Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions. Physiol Meas 31(9):1271

    Article  Google Scholar 

  • Hsu YC, Lin Y-L, Hsu W (2014) Learing-based heart rate detection from remote photoplethysmography features IEEE 2014 IEEE (ICASSP). National Taiwan University, Taipei, Taiwan

    Google Scholar 

  • Lempe G, Zaunseder S, Wirthgen T, Zipser S, Malberg H (2013) ROI selection for remote photoplethysmography. Bildverarbeitung für die medizin. Springer, Berlin, Heidelberg, pp 99–103

    Google Scholar 

  • Lin Q, Li T, Shakeel PM et al (2021) Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management. J Ambient Intell Human Comput 12:3329–3340. https://doi.org/10.1007/s12652-020-02650-3

    Article  Google Scholar 

  • Makhlouf A, Boudouane I, Saadia N et al (2019) Ambient assistance service for fall and heart problem detection. J Ambient Intell Hum Comput 10:1527–1546. https://doi.org/10.1007/s12652-018-0724-4

    Article  Google Scholar 

  • Malacarne A, Bonomi M, Pasquini C, Boato G (2016) Improved remote estimation of heart rate in face videos. In: Proceedings of GlobalSIP 2016

  • McDuff DJ, Estepp JR, Piasecki AM, Blackford EB (2015) A survey of remote optical photoplethysmographic imaging methods. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE. pp. 6398–6404

  • Nkurikiyeyezu KN, Suzuki Y, Lopez GF (2018) Heart rate variability as a predictive biomarker of thermal comfort. J Ambient Intell Hum Comput 9:1465–1477. https://doi.org/10.1007/s12652-017-0567-4

    Article  Google Scholar 

  • Noulas A, Krose B (2006) EM detection of common origin of multi-modalcues. In: Proc. ACM Conf. Multimodal Interfaces, pp. 201–208

  • Purtov K, Kublanov V, Petrenko A, Petrenko T (2016) Remote Photoplethysmography application to the analysis of time- frequency changes of human heart rate variability. In: Proceeding of the 18th conference of FRUCT association

  • Rouast PV, Adam MT, Chiong R, Cornforth D, Lux E (2018) Remote heart rate measurement using low-cost RGB face video: a technical literature review. Front Comp Sci 12(5):858–872

    Article  Google Scholar 

  • Seepers RM, Wang W, de Haan G, Sourdis I, Strydis C (2017) Attacks on heartbeat-based security using remote photoplethysmography. IEEE J Biomed Health Inform 22(7):714–721

    Google Scholar 

  • Shekar KC, Chandra P, Rao KV (2020) A framework for automatic detection of heart diseases using dynamic deep neural activation functions. J Ambient Intell Human Comput 11:5341–5352. https://doi.org/10.1007/s12652-020-01883-6

    Article  Google Scholar 

  • Singh S, Badaya S (2014) Health care in rural India: a lack between need and feed article. South Asian J Cancer 3(2):143–144

    Article  Google Scholar 

  • ThangaSelvi R, Muthulakshmi I (2020) An optimal artificial neural network based big data application for heart disease diagnosis and classification model. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02181-x

    Article  Google Scholar 

  • Tsouri GR, Kyal S, Dianat SA, Mestha LK (2012) Constrained independent component analysis approach to nonobtrusive pulse rate measurements. J Biomed Opt 17(7):077011

    Article  Google Scholar 

  • Uthaya Kumar C, Kamalraj S (2020) Ambient intelligence architecture of MRPM context based 12-tap further desensitized half band FIR filter for EEG signal. J Ambient Intell Hum Comput 11:1459–1466. https://doi.org/10.1007/s12652-019-01237-x

    Article  Google Scholar 

  • Zanetti M, Mizumoto T, Faes L et al (2021) Multilevel assessment of mental stress via network physiology paradigm using consumer wearable devices. J Ambient Intell Hum Omput 12:4409–4418. https://doi.org/10.1007/s12652-019-01571-0

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, KLN College of Engineering, Sivagangai, Tamil Nadu, India

    R. Karthick

  2. Department of ECE, Sethu Institute of Technology, Virudhunagar, Tamil Nadu, India

    M. Sheik Dawood

  3. Department of EEE, Sethu Institute of Technology, Virudhunagar, India

    P. Meenalochini

Authors
  1. R. Karthick
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Sheik Dawood
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Meenalochini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Karthick.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karthick, R., Dawood, M.S. & Meenalochini, P. Analysis of vital signs using remote photoplethysmography (RPPG). J Ambient Intell Human Comput 14, 16729–16736 (2023). https://doi.org/10.1007/s12652-023-04683-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-023-04683-w

Keywords

Search

Navigation