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Intelligent Multi-Sensor System for Remote Detection of COVID-19

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Smart and Sustainable Technology for Resilient Cities and Communities

Part of the book series: Advances in Sustainability Science and Technology ((ASST))

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Abstract

The worldwide spread of COVID-19 pandemic creates an urgent need for research and development of safe and efficient solutions for early COVID-19 detection. In this paper, an intelligent, reliable, and low-cost system detecting the main symptoms of COVID-19 disease (fever, cough, and breathing difficulties) is proposed. This system applies the principle of multi-sensor data fusion to provide a robust, precise, and complementary analysis between these symptoms to tell whether or not an individual is a carrier of COVID-19 disease. Using machine learning tools, the system is trained on infrared images to recognize the fever. The obtained thermal images are also used to control the breathing rate by monitoring temperature changes around the nasal areas on the faces. This signature is recognized through a well-trained thermal image processing model from online databases. To identify the third symptom of COVID-19 (cough), the system is associated with a network of microphones. Using specific artificial intelligence (AI) model based on mel-frequency cepstral coefficients (MFCC) convolutional neural network (CNN) architecture, it is possible to detect the cough sound. The combined use of the thermal and sound sensors allows merging data of the multi-sensor system. This approach is often the most suitable response to operational needs requiring a complete, efficient, and reactive diagnosis. The system presented in this paper is designed to be used for public hosting institutions. The objective is to contribute to slowing or even stopping the spread of COVID-19. This system can also be adapted as a useful means of early detection of many other diseases.

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References

  1. Hashmi HAS, Asif HM (2020) Early detection and assessment of covid-19. Front Med 7:311. https://doi.org/10.3389/fmed.2020.00311

  2. Chakkor S, Baghouri M, Cheker Z, el Oualkadi A, el Hangouche JA, Laamech J (2020) Intelligent network for proactive detection of COVID-19 disease. In: 2020 6th IEEE congress on information science and technology (CiSt), IEEE, Agadir - Essaouira, Morocco, pp 472–478. https://doi.org/10.1109/CiSt49399.2021.9357181

  3. Coronavirus disease (COVID-19) pandemic. https://www.who.int/emergencies/diseases/novel-coronavirus-2019?gclid=EAIaIQobChMIyfrYpOjB8AIVkbrtCh1jqQddEAAYASAAEgI7gvD_BwE. Last accessed 2021/05/11

  4. Mehrdad S, Wang Y, Atashzar SF (2021) Perspective: wearable internet of medical things for remote tracking of symptoms, prediction of health anomalies, implementation of preventative measures, and control of virus spread during the era of COVID-19. Front Robot AI 8:610653. https://doi.org/10.3389/frobt.2021.610653

  5. Scientific and Technological Research Support Program in connection with “Covid-19”. https://www.cnrst.ma/index.php/fr/component/k2/item/433-programme-de-soutien-a-la-recherche-scientifique-e-t-technologique-en-lien-avec-le-covid-19. Last accessed 2021/05/11

  6. Sund-Levander M, Forsberg C, Wahren LK (2002) Normal oral, rectal, tympanic and axillary body temperature in adult men and women: a systematic literature review. Scand J Caring Sci 16(2):122–128. https://doi.org/10.1046/j.1471-6712.2002.00069.x. PMID: 12000664

  7. Wang C, Horby PW, Hayden FG, Gao GF (2020) A novel coronavirus outbreak of global health concern. Lancet 395(10223):15–21, 470–473

    Google Scholar 

  8. Temperature Screening Thermographic Turret Camera: https://www.hikvision.com/fr/products/Thermal-Products/Thermography-thermal-cameras/temperature-screening-series/ds-2td1217b-3-pa/. Last accessed 2021/05/13

  9. Lee AW, Hu Q (2005) Real-time, continuous-wave terahertz imaging by use of a microbolometer focal-plane array. Opt Lett 30:2563–2565

    Article  Google Scholar 

  10. Sharma N, Krishnan P, Kumar R, Ramoji S, Chetupalli SR, Ghosh PK, Ganapathy S (2020) Coswara—a database of breathing, cough, and voice sounds for COVID-19 diagnosis. Interspeech 4811–4815. https://doi.org/10.21437/Interspeech.2020-2768

  11. Li S-H, Lin B-S, Tsai C-H, Yang C-T, Lin B-S (2017) Design of wearable breathing sound monitoring system for real-time wheeze detection. Sensors 17:171. https://doi.org/10.3390/s17010171

  12. Oletic D, Bilas V (2016) Energy-efficient respiratory sounds sensing for personal mobile asthma monitoring. IEEE Sens J 16(23):8295–8303

    Google Scholar 

  13. Toop LJ, Thorpe CW, Fright R (1989) Cough sound analysis: a new tool for the diagnosis of asthma. Fam Pract 6:83–85

    Article  Google Scholar 

  14. Deshpande G, Schuller BW (2020) An overview on audio, signal, speech, & language processing for COVID-19. arXiv:2005.08579v1 [cs.CY] 5 pages

  15. Imran A, Posokhova I, Qureshi HN, Masood U, Riaz MS, Ali K, John CN, Hussain MI, Nabeel M (2020) AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform Med Unlocked 20:100378. https://doi.org/10.1016/j.imu.2020.100378

    Article  Google Scholar 

  16. Brown C, Chauhan J, Grammenos A, Han J, Hasthanasombat A, Spathis D, Xia T, Cicuta P, Mascolo C (2020) Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. Presented at the KDD’20: The 26th ACM SIGKDD conference on knowledge discovery and data mining, ACM, Virtual Event CA USA, pp 3474–3484. https://doi.org/10.1145/3394486.3412865

  17. Carnegie Mellon University Home Page: https://cvd.lti.cmu.edu/. Last accessed 2021/04/27

  18. COVID-19 Sounds App, University of Cambridge. https://www.covid-19-sounds.org/en/. Last accessed 2021/04/27

  19. Magni C, Chellini E, Lavorini F, Fontana GA, Widdicombe J (2011) Voluntary and reflex cough: similarities and differences. Pulm Pharmacol Ther 24(3):308–311. https://doi.org/10.1016/j.pupt.2011.01.007

    Article  Google Scholar 

  20. Han J, Qian K, Song M, Yang Z, Ren Z, Liu S, Liu J, Zheng H, Ji W, Koike T, Li X, Zhang Z, Yama-moto Y, Schuller B (2020) An early study on intelligent analysis of speech under COVID-19: severity, sleep quality, fatigue, and anxiety. arXiv:2005.00096 [eess.AS] 5 pages

  21. Cohen-McFarlane M, IEEE Student Member, Goubran R, IEEE Fellow, and Knoefe F (2020) Novel coronavirus (2019) cough database: NoCoCoDa. IEEE ACESS 8:154087–154094. https://doi.org/10.1109/ACCESS.2020.3018028

  22. The Importance of Respiratory Rate Tracking During The COVID-19 Pandemic. https://www.whoop.com/thelocker/respiratory-rate-tracking-coronavirus/. Last accessed 2021/04/27

  23. Miller DJ, Capodilupo JV, Lastella M, Sargent C, Roach GD, Lee VH, et al (2020) Analyzing changes in respiratory rate to predict the risk of COVID-19 infection. PLoS ONE 15(12):e0243693. https://doi.org/10.1371/journal.pone.0243693

  24. Kranthi KL, Alphonse PJA (2021) A literature review on COVID-19 disease diagnosis from respiratory sound data. AIMS Bioeng 8(2):140–153. https://doi.org/10.3934/bioeng.2021013

  25. Ghahramani G, Castro G, Karvigh SA, Becerik-Gerber B (2018) Towards unsupervised learning of thermal comfort using infrared thermography. Appl Energy 211:41–49. ISSN: 0306-2619. https://doi.org/10.1016/j.apenergy.2017.11.021

  26. Tan JH, Ng EYK, Rajendra Acharya U, Chee C (2009) Infrared thermography on ocular surface temperature: a review. Infrared Phys Technol 52(4):97–108. ISSN: 1350-4495. https://doi.org/10.1016/j.infrared.2009.05.002

  27. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features

    Google Scholar 

  28. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vision 57(2):137–154

    Article  Google Scholar 

  29. Gemmeke JF, Ellis DPW, Freedman D, Jansen A, Lawrence W, Moore RC, Plakal M, Ritter M (2017) Audio set: an ontology and human-labeled dataset for audio events. In: Proceedings of IEEE ICASSP 2017. New Orleans, LA

    Google Scholar 

  30. Freesound—Sounds browse, n.d. https://freesound.org/browse/. Last accessed 09 May 2021

  31. Virufy T (n.d.) Home|Virufy. https://virufy.org//en/ https://freesound.org/browse/, last accessed 09 May 2021

  32. Matos S, Member S, Birring SS, Pavord ID, Evans DH, Member S (2006) Detection of cough signals in continuous audio recordings using hidden Markov models. IEEE Trans Biomed Eng 53(6):1078–1083

    Article  Google Scholar 

  33. Drugman T, Urbain J, Dutoit T (2011) Assessment of audio features for automatic cough detection. In: Proceedings of 19th European signal processing conference, no. 32

    Google Scholar 

  34. Larson EC, Lee T, Liu S, Rosenfeld M, Patel SN (2011) Accurate and privacy preserving cough sensing using a low-cost microphone. In: Proceedings of 13th international conference on ubiquitous computing, p 375

    Google Scholar 

  35. Pereira C, Yu X, Czaplik M, Rossaint R, Blazek V, Leonhardt S (2015) Remote monitoring of breathing dynamics using infrared thermography. Biomed Opt Express 6:4378–4394

    Google Scholar 

  36. Ruminski J, Kwasniewska A (2017) Evaluation of respiration rate using thermal imaging in mobile conditions. In: Ng EY, Etehadtavakol M (eds) Application of infrared to biomedical sciences. Series in bioengineering. Springer, Singapore, pp 311–346. https://doi.org/10.1007/978-981-10-3147-2_18

  37. Lehtola V, Huttunen H, Christophe F, Mikkonen T (2017) Evaluation of visual tracking algorithms for embedded devices. In: Scandinavian conference on image analysis, vol 10269. Lecture notes in computer science. Springer, pp 88–97. https://doi.org/10.1007/978-3-319-59126-1_8

  38. TrackerMedianFlow Class Reference on Open-Source Computer Vision Homepage, https://docs.opencv.org/3.4/d7/d86/classcv_1_1TrackerMedianFlow.html. Last accessed 2021/04/28

  39. Video used for test: Thermal Body Temp Monitoring Solution—Dahua. https://youtu.be/VJy2869i_K8. Last accessed 2021/04/27

  40. Cho Y, Bianchi-Berthouze N (2019) Physiological and affective computing through thermal imaging: a survey. arXiv e-prints

    Google Scholar 

  41. Chatrzarrin H, Arcelus A, Goubran R, Knoefel F (2011) Feature extraction for the differentiation of dry and wet cough sounds. In: IEEE international symposium on medical measurements and applications. IEEE

    Google Scholar 

  42. Hershey S, Chaudhuri S, Ellis DPW, Gemmeke JF, Jansen A, Moore RC, Plakal M, Platt D, Saurous RA, Seybold B, Slaney M, Weiss RJ, Wilson K (2016) CNN architectures for large-scale audio classification

    Google Scholar 

  43. Hu MH, Zhai GT, Li D, Fan YZ, Chen XH, Yang XK (2017) Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement. J Biomed Opt 22(3):36006. https://doi.org/10.1117/1.JBO.22.3.036006. PMID: 28264083

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Acknowledgements

This work is financially supported by the Moroccan Ministry of National Education, Professional Formation, Higher Education and Scientific Research, the National Center for Scientific and Technical Research of Morocco, and the University Sidi Mohamed Ben Abdellah of Fez, we warmly thank them for this opportunity.

Also, we would like to thank our colleagues thank Pr. H. BEJJIT, Pr. C. Benjelloun, Pr. H. Saikouk, Pr. C. AlaouI, Pr. M. Ouazzani Jamil and Pr. A. Lakhssassi and our students Nour Meyazi, Ismail Laissaoui, Houria El Ansari, Imane Zakri, Taha Jadid, Youssra Derraz, Mehdi Samouh, Akram El Hachimi, Youssef El Kantri, Anas Mansouri, Ghita Ouazzani Taybi, Khalid Aoujdad, Bahija Tantaoui, Hamza Amraoui, Imane El Amri, Hafizah Aboubacar Attaou, Nezha Elbourkhissi, and Zakaria Mnah for their fruitful contributions and discussion in the framework of this project.

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Authors and Affiliations

  1. LSIGER, FST-FES, Sidi Mohammed Ben Abdellah University, Fez, Morocco

    G. Zaz, A. Mechaqrane, M. Jorio & A. Ahaitouf

  2. LSIGER, ENSA-FES, Sidi Mohammed Ben Abdellah University, Fez, Morocco

    M. Alami Marktani, A. Elboushaki, Y. Farhane, S. Bennani Dosse & A. Mansouri

  3. LESETI, ENSA-OUJDA, Mohamed Premier University, Oujda, Morocco

    H. Bekkay

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  1. G. Zaz
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Correspondence to G. Zaz .

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Editors and Affiliations

  1. KES International Research, Shoreham-by-sea, UK

    Robert J. Howlett

  2. KES International, Selby, UK

    Lakhmi C. Jain

  3. Cardiff Metropolitan University, Wales, UK

    John R. Littlewood

  4. Aurel Vlaicu University, Arad, Romania

    Marius M. Balas

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Zaz, G. et al. (2022). Intelligent Multi-Sensor System for Remote Detection of COVID-19. In: Howlett, R.J., Jain, L.C., Littlewood, J.R., Balas, M.M. (eds) Smart and Sustainable Technology for Resilient Cities and Communities. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-9101-0_11

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