Non-contact Respiration Monitoring Using Bio- Radar Sensor Based on Linear Regression Classifier

Muhamad Fahrudin Y.; Syaifudin Syaifudin; Bambang Guruh Irianto; Phuoc-Hai Huynh

doi:10.35882/teknokes.v17i1.664

Muhamad Fahrudin Y. Departement of Medical Electronic Technology, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia
Syaifudin Syaifudin Departement of Medical Electronic Technology, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia https://orcid.org/0000-0002-3922-4429
Bambang Guruh Irianto Departement of Medical Electronic Technology, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia https://orcid.org/0000-0002-1613-4762
Phuoc-Hai Huynh Faculty of Information Technology, Angiang University, Long Xuyen city, An Giang province, VIETNAM https://orcid.org/0000-0001-8348-9267

DOI: https://doi.org/10.35882/teknokes.v17i1.664

Keywords: Respiration, Bio-Radar, Artificial Intelligence, Linear Regression

Abstract

Tuberculosis (TB) is an infectious disease that mainly attacks the lungs, caused by the bacterium Mycobacterium tuberculosis. To reduce its spread, hospitals use special rooms for TB patients and health workers follow strict Standard Operating Procedures (SOP). Recent advances in medical technology have led to the development of contactless respiratory monitoring techniques, such as bio-radar sensors that utilize the Doppler principle to detect lung movement. This research aims to explore the application of bio-radar sensors for contactless respiratory rate monitoring and then combine it with machine learning methods, specifically using linear regression algorithms, to translate bio-radar output into measurable respiratory rate values. By training a regression model using a processed raw data set to identify inspiration and expiration, where 1 is inspiration and 0 is expiration. To test the performance of the contactless breathing module, it was compared to a patient monitor. The module and comparison tool were run simultaneously with 10 measurement distance points for 10 patients or respondents with each distance point taken three times. The data that has been obtained from the results of comparisons between modules and comparison tools is entered into machine learning data analysis techniques, namely accuracy, precision and recall. The accuracy results were 74.9%, precision 71.4% and recall 83.3%. This research has proven that bio radar can be used to detect lung movement.

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References

L. Disease, “Journal of Tuberculosis and Lung Disease 35 th World Conference on Lung Health of the Tuberculosis and Lung Disease PA R I S • F R A N C E Awards of the International Union Against,” vol. 8, no. 11, 2004.

P. A. Siregar, Y. K. Ashar, R. R. A. Hasibuan, F. Nasution, F. Hayati, and N. Susanti, “Improvement of Knowledge and Attitudes on Tuberculosis Patients with Poster Calendar and Leaflet,” J. Heal. Educ., vol. 6, no. 1, pp. 39–46, 2021, doi: 10.15294/jhe.v6i1.42898.

S. Ravimohan, H. Kornfeld, D. Weissman, and G. P. Bisson, “Tuberculosis and lung damage: From epidemiology to pathophysiology,” Eur. Respir. Rev., vol. 27, no. 147, 2018, doi: 10.1183/16000617.0077-2017.

T. J. Nagu et al., “Clinical and Imaging Features of Adults with Recurrent Pulmonary Tuberculosis - A Prospective Case-Controlled Study,” Int. J. Infect. Dis., vol. 113, no. xxxx, pp. S33–S39, 2021, doi: 10.1016/j.ijid.2021.01.071.

S. M. Maretbayeva et al., “Culture conversion at six months in patients receiving bedaquiline- and delamanid-containing regimens for the treatment of multidrug-resistant tuberculosis,” Int. J. Infect. Dis., vol. 113, no. xxxx, pp. S91–S95, 2021, doi: 10.1016/j.ijid.2021.03.075.

H. Chen, K. Liu, Z. Li, and P. Wang, “Point of care testing for infectious diseases,” Clin. Chim. Acta, vol. 493, no. January, pp. 138–147, 2019, doi: 10.1016/j.cca.2019.03.008.

A. Zumla et al., “World Tuberculosis Day 2021 Theme — ‘The Clock is Ticking’ — and the world is running out of time to deliver the United Nations General Assembly commitments to End TB due to the COVID-19 pandemic,” Int. J. Infect. Dis., vol. 113, pp. S1–S6, 2021, doi: 10.1016/j.ijid.2021.03.046.

A. Nurjannah et al., “Determinan Sosial Tuberculosis di Indonesia,” J. Penelit. dan Pengemb. Kesehat. Masy. Indones., vol. 3, no. 1, pp. 65–76, 2022, [Online]. Available: https://journal.unnes.ac.id/sju/index.php/jppkmi

A. N. Ayu Merzistya, M. Sakundarno Adi, D. Sutiningsih, and S. R. Rahayu, “the Quality of Tuberculosis Services in Patients’ Perspectives: a Literature Review,” Indones. J. Heal. Adm., vol. 9, no. 1, pp. 67–81, 2021, doi: 10.20473/jaki.v9i1.2021.67-81.

R. Kukkapalli, N. Banerjee, R. Robucci, and Y. Kostov, “Micro-radar wearable respiration monitor,” Proc. IEEE Sensors, vol. 0, pp. 1–3, 2016, doi: 10.1109/ICSENS.2016.7808741.

S. He, Z. Han, C. Iglesias, V. Mehta, and M. Bolic, “A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars,” Front. Physiol., vol. 13, no. March, pp. 1–14, 2022, doi: 10.3389/fphys.2022.799621.

B. S. Embc, A. Bermeo, S. Member, M. Bravo, S. Member, and M. Huerta, “The 38th Annual International Conference of the IEEE Engineering in Medicine A System to Monitor Tremors in Patients with Parkinson ’ s Disease,” no. September, 2016.

S. Liang, P. Sutham, K. Wu, K. Mallikarjunan, and J. P. Wang, “Giant Magnetoresistance Biosensors for Food Safety Applications,” Sensors, vol. 22, no. 15, 2022, doi: 10.3390/s22155663.

S. Abdulatif, F. Aziz, P. Altiner, B. Kleiner, and U. Schneider, “Power-Based Real-Time Respiration Monitoring Using FMCW Radar,” 2017, [Online]. Available: http://arxiv.org/abs/1711.09198

B. Isa Bakare, M. Ajaegbu, B. Bakare, M. Ajaegbu, and V. Idigo, “A Comprehensive Review of Radar System Technology,” Quest Journals J. Electron. Commun. Eng. Res., vol. 8, no. 8, pp. 2321–5941, 2022, [Online]. Available: www.questjournals.org

S. Pun, “How Radar Technology Changed the Course of the World after World War II - Science and Technology,” Unity J., vol. 2, no. February, pp. 243–250, 2021, doi: 10.3126/unityj.v2i0.38847.

H. G. A. Waode Erimelga and and Sari Luthfiyah, “A Pioneering Study on the Design and Implementation of Bioradar Sensors for Luxurious Portable Non-Contact Respiration Monitoring,” J. Teknokes, vol. 16, no. 2, pp. 51–57, 2023.

S. Malik, M. Ahmad, M. Punjiya, A. Sadeqi, M. S. Baghini, and S. Sonkusale, “Respiration Monitoring Using a Flexible Paper-Based Capacitive Sensor,” Proc. IEEE Sensors, vol. 2018-Octob, pp. 1–4, 2018, doi: 10.1109/ICSENS.2018.8589558.

A. A. Pramudita and F. Y. Suratman, “Low-Power Radar System for Noncontact Human Respiration Sensor,” IEEE Trans. Instrum. Meas., vol. 70, 2021, doi: 10.1109/TIM.2021.3087839.

A. Tataraidze, L. Anishchenko, M. Alekhin, L. Korostovtseva, and Y. Sviryaev, “Estimation of respiratory rhythm during night sleep using a bio-radar,” Radar Sens. Technol. XVIII, vol. 9077, p. 90770Z, 2014, doi: 10.1117/12.2049519.

H. Stratton, R. Saatchi, R. Evans, and H. Elphick, “Noncontact respiration rate monitoring: An evaluation of four methods,” 17th Int. Conf. Cond. Monit. Asset Manag. C. 2021, pp. 0–10, 2021.

F. Yang, Z. He, Y. Fu, L. Li, K. Jiang, and F. Xie, “Noncontact detection of respiration rate based on forward scatter radar,” Sensors (Switzerland), vol. 19, no. 21, 2019, doi: 10.3390/s19214778.

N. S. Pezol, R. Adnan, and M. Tajjudin, “2020 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2020 - Proceedings,” 2020 IEEE Int. Conf. Autom. Control Intell. Syst. I2CACIS 2020 - Proc., no. June, pp. 69–73, 2020.

M. J. Parmar, A. Vala, A. Patel, and H. Patel, “A Brief Review on Bio-Medical Application of Radar,” Int. J. Eng. Sci. Invent., vol. 7, no. 4, pp. 66–72, 2018.

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. Electromed. Eng. Med. Informatics, vol. 4, no. 2, pp. 70–76, 2022, doi: 10.35882/jeeemi.v4i2.3.