[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Touchless Pulse Diagnostics Methods and Devices: A Review

  • Conference paper
  • First Online:
Information Technology in Biomedicine (ITIB 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1429))

Included in the following conference series:

  • 402 Accesses

Abstract

Noninvasive monitoring of human vital parameters is a widely studied topic. The scientists and engineers create many devices with telemedicine applications. Also in everyday functioning people use gadgets that contain noninvasive measurements (e.g. heart rate measurements in a smart watch). In addition to medical diagnostics, they are also used to monitor sports development. This paper presents a literature review on the noninvasive measurement of human vital signs. Our goal is to present methods that allow monitoring of human vital parameters and provide examples of applications in the constructed devices.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 159.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 199.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Mazurek, T.: Wskazania diagnostyczne do cewnikowania jam serca, zasady zabiegu, pp. 202–205. Gdańsk, Via Medica (2013)

    Google Scholar 

  2. Siedlecka, A., Ciach, K., Świątkowska-Frerund, M., Preis, K.: Fear related to aminocentesis as a method if invasive prenatal diagnosis. GinPolMedProject 4(18), 38–43 (2010)

    Google Scholar 

  3. Pawełczyk, K., Marciniak, M., Kołodziej, J.: Invasive diagnostics of throatic malignant diseases. Adv. Clin. Exp. Med. 13(6), 1067–1072 (2004)

    Google Scholar 

  4. Swora, E., Stankowiak-Kulpa, H., Marcinkowska, E., Grzymisławski, M.: Clinical aspects of diagnostics in heliobacter pylori infection. Nowiny Lekarskie 78(3–4), 228–230 (2009)

    Google Scholar 

  5. Castaneda, D., Esparza, A., Ghamari, M., Soltanpur, C., Nazezran, H.: A review on wearable photoplethysmography sensors and their potential future applications in health care. Int. J. Biosens. Bioelectron. 4(4), 195–202 (2018)

    Google Scholar 

  6. Celka, P., Charlton, P.H., Farukh, B., Chowienczyk, P., Alastruey, J.: Influence of mental stress on the pulse wave features of photoplethysmograms. Healthcare Technol. Lett. 7(1), 7–12 (2020)

    Article  Google Scholar 

  7. Hong, S., Park, K.S.: Unobtrusive photoplethysmographic monitoring under the foot sole while in a standing posture. Sensors 18, 3239 (2018)

    Article  Google Scholar 

  8. Prokop, D.: Zastosowanie wieloczujnikowego optoelektronicznego systemu pomiarowego do badania przebiegów fali tętna (2017)

    Google Scholar 

  9. Nabeel, P.M., Jayaraj, J., Mohansankar, S.: Single-source PPG based local pulse wave velocity measurement: a potential cuffless blood pressure estimation technique. Physiol. Meas. 38(12), 2122–2140 (2017)

    Article  Google Scholar 

  10. Poh, M.-Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011)

    Article  Google Scholar 

  11. Poh, M.-Z., McDuff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Exp. 18(10), 10762–10774 (2010)

    Article  Google Scholar 

  12. Couderc, J.-P., et al.: Detection of atrial fibrillation using contactless facial video monitoring. Heart Rhythm 12(1), 195–201 (2015)

    Article  Google Scholar 

  13. Couderc, J.-P., et al.: Pulse harmonic strength of facial video signal for the detection of atrial fibrillation. Comput. Cardiol. 41, 661–664 (2014)

    Google Scholar 

  14. Sugita, N., et al.: Estimation of absolute blood pressure using video images captured at different heights from the heart. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2019)

    Google Scholar 

  15. Przybyło, J., Kańoch, E., Jabłoński, M., Augustyniak, P.: Distant measurements of plethysmographic signal in various lighting conditions using configurable frame-rate camera. Metrol. Meas. Syst. 23(4), 579–592 (2016)

    Article  Google Scholar 

  16. Mędrala, R., Augustyniak, P.: Taking Videoplethysmographic Measurements at Alternative Parts of the Body - Pilot Study, PCBBE (2019)

    Google Scholar 

  17. Królak, A.: Influence of skin tone on efficiency of vision-based heart rate estimation. In: Augustyniak, P., Maniewski, R., Tadeusiewicz, R. (eds.) PCBBE 2017. AISC, vol. 647, pp. 44–55. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-66905-2_4

    Chapter  Google Scholar 

  18. Nabeel, P.M., Jayaraj, J., Mohanasankar, S.: Single-source PPG based local pulse wave velocity measurement: a potential cuffess blood pressure estimation technique. Inst. Phys. Eng. Med. 38(12), 2122–2140 (2017)

    Google Scholar 

  19. Al-Naji, A., Perera, A.G., Chahl, J.: Remote monitoring of cardiorespiratory signals from a hovering unmanned aerial vehicle. BioMed. Eng. OnLine 16, 101 (2017). https://doi.org/10.1186/s12938-017-0395-y

    Article  Google Scholar 

  20. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, p. 511. IEEE (2001)

    Google Scholar 

  21. Przybyło, J.: Continuous distant measurement of the user’s heart rate in human-computer interaction applications. Sensors 19, 4205 (2019)

    Article  Google Scholar 

  22. Wu, J.H., Chang, R.S., Jiang, J.A.: A novel pulse measurement system by using laser triangulation and a CMOS image sensor. Sensors 7(12), 3366–3385 (2007). https://doi.org/10.3390/s7123366

    Article  Google Scholar 

  23. Antognoli, L., Moccia, S., Migliorelli, L., Casaccia, S., Scalise, L., Frontoni, E.: Heartbeat detection by laser doppler vibrometry and machine learning. Sensors. 20(18), 5362 (2020)

    Article  Google Scholar 

  24. Lin, J.C.: Noninvasive microwave measurement of respiration. IEEE 63(10), 1530–1530 (1975)

    Article  Google Scholar 

  25. Ren, L., et al.: Phase based methods for heart rate detection using UWB impulse doppler radar. IEEE Trans. Microwave Theor. Tech. 64(10), 3319–3331 (2016)

    Article  Google Scholar 

  26. Rong, Y., Herschfelt, A., Holtom, J., Bliss, D.W.: Cardiac and respiratory sensing from a hovering UAV radar platform. In: 2021 IEEE Statistical Signal Processing Workshop (2021)

    Google Scholar 

  27. Abdulatif, S., et al.: Power-based real-time respiration monitoring using FMCW radar. Comput. Sci. Eng. (2017)

    Google Scholar 

  28. Regev, N., Wulich, D.: Radar-based, simultaneous human presence detection and breathing rate estimation. Sensors 21, 3529 (2021)

    Article  Google Scholar 

  29. Michahelles, F., Wicki, R., Schiele, B.: Less contact: heart-rate detection without even touching the user. In: Eighth International Symposium on Wearable Computers (2004)

    Google Scholar 

  30. Ravichandran, R.: et al., WiBreathe: estimating respiration rate using wireless signals in natural settings in the home. In: 2015 IEEE International Conference on Pervasive Computing and Communications (2015)

    Google Scholar 

  31. Jasińki, Ł.: Pomiar tłumienia ścian i innych elementów charakterystycznych dla środowiska wewnątrzbudynkowego w paśmie 2,4 GHz, www.alvarus.org (2011)

  32. Liu, J., et al.: Recent progress in flexible wearable sensors for vital sign monitoring. Sensors 20, 4009 (2020)

    Article  Google Scholar 

  33. Qiu, S., Wang, Z., Zhao, H., Hu, H.: Using distributed wearable sensors to measure and evaluate human lower limbs motion. IEEE Trans. Instrum. Measur. 65(4), 939–950 (2016)

    Article  Google Scholar 

  34. Weich, C., Vieten, M.M.: The Gaitprint: identifying individuals by their running style. Sensors 20, 3810 (2020)

    Article  Google Scholar 

  35. Petersen, J., Austin, D., Sack, R., Hayes, T.L.: Actigraphy-based scratch detection using logistic regression. IEEE J. Biomed. Health Inf. 17(2), 277–283 (2013)

    Article  Google Scholar 

  36. Zhang, P., Zhang, Z., Chao, H.-C.: A stacked human activity recognition model based on parallel recurrent network and time series evidence theory. Sensors 20, 4016 (2020)

    Article  Google Scholar 

  37. Pitou, S., Michael, B., Thompson, K., Howard, M.: Hand-Made embroidered electromyography: towards a solution for low-income countries. Sensors 20, 3347 (2020)

    Article  Google Scholar 

  38. Chen, Z., Zhu, Q., Soh, Y.C., Zhang, L.: Roboust human activity recognition using smartphone sensors svia CT-PCA and online SVM. IEEE Trans. Ind. Inf. 13(6), 3070–3080 (2017)

    Article  Google Scholar 

  39. Huang, S.-J., Wu, C.-J., Chen, C.-C.: Pattern recognition of human postures using the data density functional method. Appl. Sci. 8, 1615 (2018)

    Article  Google Scholar 

  40. Hossain, T., Ahad, A.R., Inoue, S.: A method for sensor-based activity recognition in missing data scenario. Sensors 20, 3811 (2020)

    Article  Google Scholar 

  41. Horn, B.K.P.: Observation model for indoor positioning. Sensors 20, 4027 (2020)

    Article  Google Scholar 

  42. Kańtoch, E.: Recognition of sedentary behaviour by machine learning analysis of wearable sensors during activities of daily living for telemedical assessment of cardiovascular risk. Sensors 18, 3219 (2018)

    Article  Google Scholar 

  43. Zapata, J., Fernández-Luque, F.J., Ruiz, R.: Wireless sensor network for ambient assisted living, December 2010. ISBN 978-953-307-321-7, https://doi.org/10.5772/13005

  44. Zhang, J., Xue, N., Huang, X.: A secure system for pervasive social network-based healthcare. IEEE Access 4, 9239–9250 (2016)

    Article  Google Scholar 

  45. Chen, M., Zhang, Y., Li, Y., Hassan, M.M., Alamri, A.: AIWAC: affective interaction through wearable computing and cloud technology. IEEE Wirel. Commun. 22(1), 20–27 (2015)

    Google Scholar 

  46. Norouzi, N., Bruder, G., Belna, B., Mutter, S., Turgut, D., Welch, G.: A systematic review of the convergence of augmented reality, intelligent virtual agents, and the internet of things. In: Al-Turjman, F. (ed.) Artificial Intelligence in IoT. TCSCI, pp. 1–24. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04110-6_1

    Chapter  Google Scholar 

Download references

Acknowledgement

Research supported by the AGH University of Science and Technology in year 2022 from the subvention granted by the Polish Ministry of Science and Higher Education; grant no. 16.16.120.773

Author information

Authors and Affiliations

  1. Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza Avenue Building C-3, 30-059, Krakow, Poland

    Anna Pająk & Piotr Augustyniak

Authors
  1. Anna Pająk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr Augustyniak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anna Pająk .

Editor information

Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Wojciech Wieclawek

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pająk, A., Augustyniak, P. (2022). Touchless Pulse Diagnostics Methods and Devices: A Review. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_31

Download citation

Publish with us

Policies and ethics

Search

Navigation