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Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients

Year 2024, Volume: 40 Issue: 1, 34 - 44, 30.04.2024

Abstract

Respiratory diseases are usually microbial in origin. Chronic Obstructive Pulmonary Disease and Asthma are considered the most common chronic respiratory diseases. Although these diseases cannot be completely eliminated, they are preventable and treatable. The most important practices in the control of these diseases are quitting smoking, reducing indoor and outdoor air pollution, and avoiding occupational dusts and chemicals. Therefore, the first step for treatment should be to remove the patient from air polluted environments. In this study, a portable mobile supported and fuzzy logic based smart air quality warning device that can automatically measure dust concentration, air quality, humidity, temperature and carbon monoxide (CO) values of the indoor or outdoor environment has been developed. In addition, the data obtained from the sensors on the device is transmitted to a server via wifi. In this way, measured values, location and time information can be tracked via the mobile application we have developed, and the patient is warned audibly and visually in possible risky situations.

References

  • T. Ferkol and D. Schraufnagel, “The global burden of respiratory disease,” Ann Am Thorac Soc, vol. 11, no. 3, pp. 404–406, Mar. 2014, doi: 10.1513/AnnalsATS.201311-405PS.
  • P. Burney, D. Jarvis, and R. Perez-Padilla, “The global burden of chronic respiratory disease in adults,” Int J Tuberc Lung Dis, vol. 19, no. 1, pp. 10–20, Jan. 2015, doi: 10.5588/ijtld.14.0446.
  • GBD 2015 Chronic Respiratory Disease Collaborators, “Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015,” Lancet Respir Med, vol. 5, no. 9, pp. 691–706, Sep. 2017, doi: 10.1016/S2213-2600(17)30293-X.
  • “The top 10 causes of death.” Accessed: Jan. 29, 2024. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
  • F. V. Gerayeli et al., “COPD and the risk of poor outcomes in COVID-19: A systematic review and meta-analysis,” EClinicalMedicine, vol. 33, p. 100789, Mar. 2021, doi: 10.1016/j.eclinm.2021.100789.
  • S. Musafiri et al., “Prevalence of atopy, asthma and COPD in an urban and a rural area of an African country,” Respiratory Medicine, vol. 105, no. 11, pp. 1596–1605, Nov. 2011, doi: 10.1016/j.rmed.2011.06.013.
  • M. Darçın, “Association between air quality and quality of life,” Environ Sci Pollut Res, vol. 21, no. 3, pp. 1954–1959, Feb. 2014, doi: 10.1007/s11356-013-2101-3.
  • Z. Zhang et al., “Impact of haze and air pollution-related hazards on hospital admissions in Guangzhou, China,” Environ Sci Pollut Res, vol. 21, no. 6, pp. 4236–4244, Mar. 2014, doi: 10.1007/s11356-013-2374-6.
  • J. Cai et al., “Acute effects of air pollution on asthma hospitalization in Shanghai, China,” Environ Pollut, vol. 191, pp. 139–144, Aug. 2014, doi: 10.1016/j.envpol.2014.04.028.
  • S. Abraham and X. Li, “A Cost-effective Wireless Sensor Network System for Indoor Air Quality Monitoring Applications,” Procedia Computer Science, vol. 34, pp. 165–171, Jan. 2014, doi: 10.1016/j.procs.2014.07.090.
  • A. Al-Dahoud, M. Fezari, I. Jannoud, and T. A. Al-Rawashdeh, “Monitoring Metropolitan City Air-quality Using Wireless Sensor Nodes based on ARDUINO and XBEE,” Accessed: Jan. 29, 2024. [Online]. Available: https://www.semanticscholar.org/paper/Monitoring-Metropolitan-City-Air-quality-Using-on-Al-Dahoud-Fezari/e0400014e15b672bbf2342833cd35a511fcb6869
  • A. M. Husain, T. Rini, M. Haque, and M. R. Alam, “Air Quality Monitoring : The Use of Arduino and Android,” 2016. Accessed: Jan. 29, 2024. [Online]. Available: https://www.semanticscholar.org/paper/Air-Quality-Monitoring-%3A-The-Use-of-Arduino-and-Husain-Rini/45915b48d382a6619683bc69218eccd5148115f9
  • R. Kayyali et al., “Qualitative investigation into a wearable system for chronic obstructive pulmonary disease: the stakeholders’ perspective,” BMJ Open, vol. 6, no. 8, p. e011657, Aug. 2016, doi: 10.1136/bmjopen-2016-011657.
  • H. Üçgün, F. Gömbeci̇, U. Yüzgeç, and N. Yalçin, “IoT Tabanlı Platform ile Gerçek Zamanlı İç Ortam Hava Kalitesi İzleme Sistemi,” Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, vol. 7, no. 1, Art. no. 1, Jun. 2020, doi: 10.35193/bseufbd.730919.
  • J. Jo, B. Jo, J. Kim, S. Kim, and W. Han, “Development of an IoT-Based Indoor Air Quality Monitoring Platform,” Journal of Sensors, vol. 2020, p. e8749764, Jan. 2020, doi: 10.1155/2020/8749764.
  • D. A. Janeera., H. Poovizhi., S. S. Sheik Haseena., and S. Nivetha., “Smart Embedded Framework using Arduino and IoT for Real-Time Noise and Air Pollution Monitoring and Alert system,” in 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Mar. 2021, pp. 1416–1420. doi: 10.1109/ICAIS50930.2021.9396041.
  • Y. Hahm and H. Yoon, “The impact of air pollution alert services on respiratory diseases: generalized additive modeling study in South Korea,” Environ. Res. Lett., vol. 16, no. 6, p. 064048, May 2021, doi: 10.1088/1748-9326/ac002f.
  • K. Alzoubi, M. Al-bzoor, O. Aljalahma, and M. Ali, “Air Quality Monitoring and Alerting System to Help in Reducing Asthma Attack in Asthmatic Children,” presented at the International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC), SCITEPRESS, Feb. 2021, pp. 91–98. doi: 10.5220/0009971200002865.
  • L. A. Zadeh, “Fuzzy Logic, Neural Networks, and Soft Computing,” in Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems, vol. Volume 6, in Advances in Fuzzy Systems ? Applications and Theory, no. Volume 6, vol. Volume 6. , WORLD SCIENTIFIC, 1996, pp. 775–782. doi: 10.1142/9789814261302_0040.
  • J. Ahn, D. Shin, K. Kim, and J. Yang, “Indoor Air Quality Analysis Using Deep Learning with Sensor Data,” Sensors, vol. 17, no. 11, Art. no. 11, Nov. 2017, doi: 10.3390/s17112476.
  • C. Johnes, R. A. Sharpe, T. Menneer, T. Taylor, and P. Nestel, “Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England,” Int J Environ Res Public Health, vol. 20, no. 2, p. 1075, Jan. 2023, doi: 10.3390/ijerph20021075.
  • J. Buelvas, D. Múnera, D. P. Tobón V., J. Aguirre, and N. Gaviria, “Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study,” Water Air Soil Pollut, vol. 234, no. 4, p. 248, Apr. 2023, doi: 10.1007/s11270-023-06127-9.
  • A. Gressent, L. Malherbe, A. Colette, H. Rollin, and R. Scimia, “Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value,” Environment International, vol. 143, p. 105965, Oct. 2020, doi: 10.1016/j.envint.2020.105965.

Astım ve KOAH Hastaları için Bulanık Mantık Tabanlı Düşük Maliyetli Mobil Uyarı Sistemi

Year 2024, Volume: 40 Issue: 1, 34 - 44, 30.04.2024

Abstract

Solunum yolu hastalıkları genellikle mikrobiyal hastalıklardır. Kronik obstrüktif akciğer hastalığı ve astım en yaygın kronik solunum yolu hastalıkları arasındadır. Bu hastalıklar tamamen ortadan kaldırılamasa da, önlenebilir ve tedavi edilebilirler. İç ve dış mekân hava kirliliğini azaltmak ve mesleki toz ve kimyasallardan kaçınmak, bu hastalıkları önlemede en kritik uygulamalardır. Bu nedenle, hastayı tedavi etmenin ilk adımı, hastayı kirli ortamlardan uzaklaştırmak olmalıdır. Bu nedenle, bu çalışmada, iç mekân ya da dış mekânın toz konsantrasyonunu, hava kalitesini, nemini, sıcaklığını ve karbon monoksit (CO) değerlerini otomatik olarak ölçebilen taşınabilir mobil destekli hava kalitesi uyarı cihazı geliştirildi. Ayrıca, cihazın sensörlerden gelen verileri wifi aracılığıyla bir sunucuya iletilmektedir. Böylece, ölçülen değerler, konum ve zaman bilgisi mobil uygulama ile takip edilebilmektedir ve hasta potansiyel olarak tehlikeli durumlarda sesli ve görsel olarak uyarılmaktadır.

References

  • T. Ferkol and D. Schraufnagel, “The global burden of respiratory disease,” Ann Am Thorac Soc, vol. 11, no. 3, pp. 404–406, Mar. 2014, doi: 10.1513/AnnalsATS.201311-405PS.
  • P. Burney, D. Jarvis, and R. Perez-Padilla, “The global burden of chronic respiratory disease in adults,” Int J Tuberc Lung Dis, vol. 19, no. 1, pp. 10–20, Jan. 2015, doi: 10.5588/ijtld.14.0446.
  • GBD 2015 Chronic Respiratory Disease Collaborators, “Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015,” Lancet Respir Med, vol. 5, no. 9, pp. 691–706, Sep. 2017, doi: 10.1016/S2213-2600(17)30293-X.
  • “The top 10 causes of death.” Accessed: Jan. 29, 2024. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
  • F. V. Gerayeli et al., “COPD and the risk of poor outcomes in COVID-19: A systematic review and meta-analysis,” EClinicalMedicine, vol. 33, p. 100789, Mar. 2021, doi: 10.1016/j.eclinm.2021.100789.
  • S. Musafiri et al., “Prevalence of atopy, asthma and COPD in an urban and a rural area of an African country,” Respiratory Medicine, vol. 105, no. 11, pp. 1596–1605, Nov. 2011, doi: 10.1016/j.rmed.2011.06.013.
  • M. Darçın, “Association between air quality and quality of life,” Environ Sci Pollut Res, vol. 21, no. 3, pp. 1954–1959, Feb. 2014, doi: 10.1007/s11356-013-2101-3.
  • Z. Zhang et al., “Impact of haze and air pollution-related hazards on hospital admissions in Guangzhou, China,” Environ Sci Pollut Res, vol. 21, no. 6, pp. 4236–4244, Mar. 2014, doi: 10.1007/s11356-013-2374-6.
  • J. Cai et al., “Acute effects of air pollution on asthma hospitalization in Shanghai, China,” Environ Pollut, vol. 191, pp. 139–144, Aug. 2014, doi: 10.1016/j.envpol.2014.04.028.
  • S. Abraham and X. Li, “A Cost-effective Wireless Sensor Network System for Indoor Air Quality Monitoring Applications,” Procedia Computer Science, vol. 34, pp. 165–171, Jan. 2014, doi: 10.1016/j.procs.2014.07.090.
  • A. Al-Dahoud, M. Fezari, I. Jannoud, and T. A. Al-Rawashdeh, “Monitoring Metropolitan City Air-quality Using Wireless Sensor Nodes based on ARDUINO and XBEE,” Accessed: Jan. 29, 2024. [Online]. Available: https://www.semanticscholar.org/paper/Monitoring-Metropolitan-City-Air-quality-Using-on-Al-Dahoud-Fezari/e0400014e15b672bbf2342833cd35a511fcb6869
  • A. M. Husain, T. Rini, M. Haque, and M. R. Alam, “Air Quality Monitoring : The Use of Arduino and Android,” 2016. Accessed: Jan. 29, 2024. [Online]. Available: https://www.semanticscholar.org/paper/Air-Quality-Monitoring-%3A-The-Use-of-Arduino-and-Husain-Rini/45915b48d382a6619683bc69218eccd5148115f9
  • R. Kayyali et al., “Qualitative investigation into a wearable system for chronic obstructive pulmonary disease: the stakeholders’ perspective,” BMJ Open, vol. 6, no. 8, p. e011657, Aug. 2016, doi: 10.1136/bmjopen-2016-011657.
  • H. Üçgün, F. Gömbeci̇, U. Yüzgeç, and N. Yalçin, “IoT Tabanlı Platform ile Gerçek Zamanlı İç Ortam Hava Kalitesi İzleme Sistemi,” Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, vol. 7, no. 1, Art. no. 1, Jun. 2020, doi: 10.35193/bseufbd.730919.
  • J. Jo, B. Jo, J. Kim, S. Kim, and W. Han, “Development of an IoT-Based Indoor Air Quality Monitoring Platform,” Journal of Sensors, vol. 2020, p. e8749764, Jan. 2020, doi: 10.1155/2020/8749764.
  • D. A. Janeera., H. Poovizhi., S. S. Sheik Haseena., and S. Nivetha., “Smart Embedded Framework using Arduino and IoT for Real-Time Noise and Air Pollution Monitoring and Alert system,” in 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Mar. 2021, pp. 1416–1420. doi: 10.1109/ICAIS50930.2021.9396041.
  • Y. Hahm and H. Yoon, “The impact of air pollution alert services on respiratory diseases: generalized additive modeling study in South Korea,” Environ. Res. Lett., vol. 16, no. 6, p. 064048, May 2021, doi: 10.1088/1748-9326/ac002f.
  • K. Alzoubi, M. Al-bzoor, O. Aljalahma, and M. Ali, “Air Quality Monitoring and Alerting System to Help in Reducing Asthma Attack in Asthmatic Children,” presented at the International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC), SCITEPRESS, Feb. 2021, pp. 91–98. doi: 10.5220/0009971200002865.
  • L. A. Zadeh, “Fuzzy Logic, Neural Networks, and Soft Computing,” in Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems, vol. Volume 6, in Advances in Fuzzy Systems ? Applications and Theory, no. Volume 6, vol. Volume 6. , WORLD SCIENTIFIC, 1996, pp. 775–782. doi: 10.1142/9789814261302_0040.
  • J. Ahn, D. Shin, K. Kim, and J. Yang, “Indoor Air Quality Analysis Using Deep Learning with Sensor Data,” Sensors, vol. 17, no. 11, Art. no. 11, Nov. 2017, doi: 10.3390/s17112476.
  • C. Johnes, R. A. Sharpe, T. Menneer, T. Taylor, and P. Nestel, “Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England,” Int J Environ Res Public Health, vol. 20, no. 2, p. 1075, Jan. 2023, doi: 10.3390/ijerph20021075.
  • J. Buelvas, D. Múnera, D. P. Tobón V., J. Aguirre, and N. Gaviria, “Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study,” Water Air Soil Pollut, vol. 234, no. 4, p. 248, Apr. 2023, doi: 10.1007/s11270-023-06127-9.
  • A. Gressent, L. Malherbe, A. Colette, H. Rollin, and R. Scimia, “Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value,” Environment International, vol. 143, p. 105965, Oct. 2020, doi: 10.1016/j.envint.2020.105965.
There are 23 citations in total.

Details

Primary Language English
Subjects Context Learning, Biomedical Sciences and Technology, Biomedical Instrumentation, Medical Devices
Journal Section Articles
Authors

Mustafa Yurdakul 0000-0003-0562-4931

Sakir Tasdemır 0000-0002-2433-246X

Sümeyra Büşra Şengül 0000-0003-1385-0920

Early Pub Date April 30, 2024
Publication Date April 30, 2024
Submission Date January 30, 2024
Acceptance Date March 21, 2024
Published in Issue Year 2024 Volume: 40 Issue: 1

Cite

APA Yurdakul, M., Tasdemır, S., & Şengül, S. B. (2024). Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 40(1), 34-44.
AMA Yurdakul M, Tasdemır S, Şengül SB. Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. April 2024;40(1):34-44.
Chicago Yurdakul, Mustafa, Sakir Tasdemır, and Sümeyra Büşra Şengül. “Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 40, no. 1 (April 2024): 34-44.
EndNote Yurdakul M, Tasdemır S, Şengül SB (April 1, 2024) Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 40 1 34–44.
IEEE M. Yurdakul, S. Tasdemır, and S. B. Şengül, “Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, vol. 40, no. 1, pp. 34–44, 2024.
ISNAD Yurdakul, Mustafa et al. “Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 40/1 (April 2024), 34-44.
JAMA Yurdakul M, Tasdemır S, Şengül SB. Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2024;40:34–44.
MLA Yurdakul, Mustafa et al. “Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, vol. 40, no. 1, 2024, pp. 34-44.
Vancouver Yurdakul M, Tasdemır S, Şengül SB. Fuzzy Logic Based Low Cost Mobile Alert System for Asthma and COPD Patients. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2024;40(1):34-4.

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