Measurement of Vital Signs Respiratory Rate Based on Non Contact Techniques Using Thermal Camera & Web Camera with Facial Recognition

  • Raden Duta Ikrar Abadi Poltekes Kemenkes Surabaya
  • Endro Yulianto Department of Medical Electronics Engineering Technology, Poltekkes Kemenkes Surabaya
  • Triwiyanto Triwiyanto Department of Medical Electronics Engineering Technology, Poltekkes Kemenkes Surabaya https://orcid.org/0000-0003-3179-8900
  • Sandeep Kumar Gupta IIMT College of Engineering, India
  • Vugar Abdullayev Azerbaijan State Oil and Industry University, Azerbaijan
DOI: https://doi.org/10.35882/jeeemi.v4i2.3
Keywords: Breathing Rate, Non-Contact Method ROI (Region of Interest, Thermal Camera

Abstract

Examination of the respiratory rate is included in the calculation of vital sign parameters used by the medical team to determine whether a person's condition is good or not. Researchers want to develop a method of checking the respiratory rate that is easy to use by the general public and can display fast and precise results. During this pandemic, we are forced to reduce direct human-to-human contact with the aim of suppressing the exchange of viruses. From this condition, the researcher wants to develop a measuring instrument to measure the respiratory frequency with the non-contact method. This method is expected to reduce direct contact between humans and still get the results of the respiratory rate value which can be used as a parameter to determine a person's condition. To get the value of the respiratory rate, researchers have an idea by monitoring changes in temperature using a thermal camera. For the respiratory rate parameter, the researcher observed the nose area by detecting changes in expiratory and inspiration temperatures and then calculating the respiratory rate. To get these results, the researcher uses a method of detecting the face area or called face recognition and then detecting the ROI point in the area of interest in the nose area. In observing the respiratory rate, the temperature value during expiration is 31.05 °C while at the time of inspiration is 30.01 °C. This temperature difference will be carried out in the process of calculating the respiration rate value by the system made by the researcher. In the results of this study, it was found that the respiration rate module can be used as a reference with a normal use range of 60-120 cm with an error value of 1% if the distance is above 100 cm, then the results of this study are that this research can be implemented on a breathing frequency measuring instrument with a non-standard method. - contact

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References

[1] R. T. Sataloff, M. M. Johns, and K. M. Kost, The Biomedical Engineering HandBook, Second Edition. Ed. Joseph D. Bronzino, Second Edi. Boca Raton, Florida: Boca Raton: CRC Press LLC, 2000, 2000.
[2] P. For and M. Information, “Master ’ s Thesis Chantal Vilà Calopa Contact-Free Measurement of Cardiac and Respiratory Activities by using Thermal Imaging,” PHILIPS CHAIR FOR MEDICAL INFORMATION TECHNOLOGY, 2014.
[3] Y. Eddy Halim, Syamsudin, Mohammed, M. N., S. Al-Zubaidie, and A. K. Sairah, “2019 Novel Coronavirus Disease (Covid-19): Thermal Imaging System for Covid-19 Symptom Detection Using Iot Technology,” Rev. Argentina Clínica Psicológica, vol. XXIX, no. 5, pp. 234–239, 2020, doi: 10.24205/03276716.2020.1025.
[4] W. T. Chiu et al., “Infrared thermography to mass-screen suspected sars patients with fever,” Asia-Pacific J. Public Heal., vol. 17, no. 1, pp. 26–28, 2005, doi: 10.1177/101053950501700107.
[5] S. Shaikh, N. Akhter, and R. Manza, “Current trends in the application of thermal imaging in medical condition analysis,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 8, pp. 2708–2712, 2019.
[6] Z. Jiang, M. Hu, and G. Zhai, “PORTABLE HEALTH SCREENING DEVICE OF RESPIRATORY INFECTIONS Zheng,” in PORTABLE HEALTH SCREENING DEVICE OF RESPIRATORY INFECTIONS Zheng, 2020, pp. 1–2.
[7] A. H. Alkali, R. Saatchi, H. Elphick, and D. Burke, “Facial tracking in thermal images for real-time noncontact respiration rate monitoring,” in Proceedings - UKSim-AMSS 7th European Modelling Symposium on Computer Modelling and Simulation, EMS 2013, 2013, pp. 265–270, doi: 10.1109/EMS.2013.46.
[8] F. Al-Obaisi, J. Alqatawna, H. Faris, A. Rodan, and O. Al-Kadi, “Pattern Recognition of Thermal Images for Monitoring of Breathing Function,” Int. J. Control Autom., vol. 8, no. 6, pp. 381–392, 2015, doi: 10.14257/ijca.2015.8.6.37.
[9] C. B. Pereira, X. Yu, M. Czaplik, R. Rossaint, V. Blazek, and S. Leonhardt, “Remote monitoring of breathing dynamics using infrared thermography,” Biomed. Opt. Express, vol. 6, no. 11, p. 4378, 2015, doi: 10.1364/boe.6.004378.
[10] A. A. Nasser Kehtarnavaz, Fatemeh Saki, Andrian Duran, Anywher- Anythime Signals and System Laboratory, José Moura., vol. 53, no. 9. Claypool Publishers, 1981.
[11] T. C. Chiang, M. H. Tung, H. Rick, D. Kurniadi, Z. H. Wang, and G. J. Jong, “The Non-Contact Respiratory Monitoring System Using Thermal Image Processing,” in Proceedings - 3rd International Conference on Green Technology and Sustainable Development, GTSD 2016, 2016, pp. 86–92, doi: 10.1109/GTSD.2016.30.
[12] J. Fei and I. Pavlidis, “Analysis of Breathing Air Flow Patterns in Thermal Imaging,” in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2006, pp. 946–952, doi: 10.1109/IEMBS.2006.260117.
[13] B. Aubakir, B. Nurimbetov, I. Tursynbek, and H. A. Varol, “Vital sign monitoring utilizing Eulerian video magnification and thermography,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, vol. 2016-Octob, pp. 3527–3530, doi: 10.1109/EMBC.2016.7591489.
[14] X. S. Diaz, J. Mofor, R. Bhat, and R. R. Fletcher, “Smart Phone-Based Non-Contact Assessment of Human Breathing and Respiration for Diagnostic and Therapeutic Applications,” in GHTC 2018 - IEEE Global Humanitarian Technology Conference, Proceedings, 2019, pp. 0–5, doi: 10.1109/GHTC.2018.8601815.
[15] Shashank Deshpande and Yang Cai, “Imaging from and within the Vehicle,” in Cambridge Handbook of Learning Sciences, vol. II, 2017, p. 6347974.
[16] T. Acquisition and W. Areas, “Camera Networks,” in Intelligent Video Surveillance, Gérard Med., California, Riverside: Morgan &cLaypool publishers, 2009, pp. 303–303.
Published
2022-04-29
How to Cite
[1]
Raden Duta Ikrar Abadi, E. Yulianto, T. Triwiyanto, S. Kumar Gupta, and V. Abdullayev, “Measurement of Vital Signs Respiratory Rate Based on Non Contact Techniques Using Thermal Camera & Web Camera with Facial Recognition”, j.electron.electromedical.eng.med.inform, vol. 4, no. 2, pp. 70-76, Apr. 2022.
Issue
Vol 4 No 2 (2022): April
Section
Electronics

Copyright (c) 2022 Raden Duta Ikrar Abadi, Endro Yulianto, Triwiyanto Triwiyanto, Sandeep Kumar Gupta, Vugar Abdullayev

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