Abstract
IoT and Machine Learning has improved multi-fold in recent years and they have been playing a great role in healthcare systems which includes detecting, screening and monitoring of the patients. IoT has been successfully detecting different heart diseases, Alzheimer disease, helping autism patients and monitoring patients’ health condition with much lesser cost but providing better efficiency, reliability and accuracy. IoT also has a great prospect in fighting against COVID-19. This chapter discusses different aspects of IoT in aiding healthcare systems for detecting and monitoring Coronavirus patients. Two such IoT based models are also designed for automatic thermal monitoring and for measuring and real-time monitoring of heart rate with wearable IoT devices. Convolutional Neural Networks (CNN) is a Machine Learning algorithm that has been performing well in detecting many diseases including Coronary Artery Disease, Malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Detecting Corona positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two CNN models with different number of convolution layers and three other models based on ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. The proposed model performs with an accuracy of 97.5% and a precision of 97.5%. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.975 and F1-score of 97.5. It can be improved further by increasing the dataset for training the model.
Keywords
- Internet of Things (IoT)
- Sensors
- COVID-19
- Coronavirus
- Detection of COVID-19
- Deep learning
- Convolutional Neural Networks (CNN)
This is a preview of subscription content, access via your institution.
Buying options









References
Abdelgawad, A., & Yelamarthi, K. (2017). Internet of things (IoT) platform for structure health monitoring. Wireless Communications and Mobile Computing.
Catarinucci, L., De Donno, D., Mainetti, L., Palano, L., Patrono, Stefanizzi, M. L., & Tarricone, L. (2015). An IoT-aware architecture for smart healthcare systems. IEEE Internet of Things Journal, 2(6), 515–526.
Cohen, J. P., Morrison, P., & Dao, L. (2020). Covid-19 image data collection. arXiv:2003.11597. https://github.com/ieee8023/covid-chestxray-dataset
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., et al. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377.
Gunawan, T. S., Gani, M. H. H., Rahman, F. D. A., & Kartiwi, M. (2017). Development of face recognition on raspberry pi for security enhancement of smart home system. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 5(4), 317–325.
Hall, L. O., Paul, R., Goldgof, D. B., & Goldgof, G. M. (2020). Finding covid-19 from chest x-rays using deep learning on a small dataset. arXiv preprint arXiv:2004.02060.
Haque, K. F., Zabin, R., Yelamarthi, K., Yanambaka, P., & Abdelgawad, A. (2020). An IoT based efficient waste collection system with smart bins. In IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE.
Haque, K. F., Abdelgawad, A., Yanambaka, P., & Yelamarthi, K. (2020). An energy-efficient and reliable RPL for IoT. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE.
Haque, K. F., Kabir, K. H., & Abdelgawad, A. (2020). Advancement of routing protocols and applications of underwater wireless sensor network (UWSN)-a survey. Journal of Sensor and Actuator Networks, 9(2), 19 (2020).
Haque, K.F., Haque, F.F., Gandy, L., & Abdelgawad, A. (2020). Automatic detection of COVID-19 from chest X-ray images with convolutional neural networks. In 3rd IEEE International Conference on Computing, Electronics & Communications Engineering (IEEE iCCECE’20).
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).
Huang, C.-H., & Cheng, K.-W. (2014). RFID technology combined with IoT application in medical nursing system. Bulletin of Networking, Computing, Systems, and Software, 3(1), 20–24 (2014).
Istepanian, R. S., Sungoor, A., Faisal, A., & Philip, N. (2011). Internet of m-health things ‘m-IoT’. In IET Seminar on Assisted Living.
Joyia, G. J., Liaqat, R. M., Farooq, A., & Rehman, S. (2017). Internet of medical things (IOMT): Applications, benefits and future challenges in healthcare domain. Journal of Communication, 12(4), 240–247.
Kumar, K. S., & Bairavi, K. (2016). IoT based health monitoring system for autistic patients. In Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC–16’) (pp. 371–376). Springer.
Kumar, P. M., & Gandhi, U. D. (2018). A novel three-tier internet of things architecture with machine learning algorithm for early detection of heart diseases. Computers & Electrical Engineering, 65, 222–235.
Krishna, K. D., Akkala, V., Bharath, R., Rajalakshmi, P., Mohammed, A., Merchant, S., et al. (2016). Computer aided abnormality detection for kidney on FPGA based IoT enabled portable ultrasound imaging system. IRBM, 37(4), 189–197.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.
Mohammed, M., Syamsudin, H., Al-Zubaidi, S., AKS, R. R., & Yusuf, E. (2020). Novel covid-19 detection and diagnosis system using IoT based smart helmet. International Journal of Psychosocial Rehabilitation, 24(7).
Mooney, P. (2020). Chest x-ray images (pneumonia). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849.
Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (covid-19) based on deep features. Preprints 2020–030300.
Singh, R. P., Javaid, M., Haleem, A., & Suman, R. (2020). Internet of things (IoT) applications to fight against covid-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Vaishya, R., Javaid, M., Khan, I., & Haleem, A. (2020). Artificial intelligence (AI) applications for covid-19 pandemic. Diabetes & Metabolic Syndrome. Clinical Research & Review.
WHO Organization Coronavirus Disease (covid-19). (2019). https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
WHO Organization. (2020). Modes of transmission of virus causing covid-19: Implications for IPC precaution recommendations: Scientific brief, 27 March 2020. Technical report, World Health Organization.
WHO Coronavirus Disease (COVID-19) Dashboard. (2020). Geneva world health organization. https://covid19.who.int/.
WHO Coronavirus Disease (COVID-19) Newsroom. (2020). Geneva world health organization. https://www.who.int/news-room/detail/07-04-2020-who-lists-two-covid-19-tests-for-emergency-use.
Wu, Y.-C., Chen, C.-S., & Chan, Y.-J. (2020). The outbreak of covid-19: An overview. Journal of the Chinese Medical Association, 83(3), 217.
Yelamarthi, K., Aman, M. S., & Abdelgawad, A. (2017). An application-driven modular IoT architecture. Wireless Communications and Mobile Computing, 2017, 2017.
Yu, L., Lu, Y., & Zhu, X. (2012). Smart hospital based on internet of things. Journal of Networks, 7(10), 1654.
Zhang, H., Li, J., Wen, B., Xun, Y., & Liu, J. (2018). Connecting intelligent things in smart hospitals using NB-IoT. IEEE Internet of Things Journal, 5(3), 1550–1560.
Zhang, J., Xie, Y., Li, Y., Shen, C., & Xia, Y. (2020). Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv preprint arXiv:2003.12338.
Acknowledgements
The authors would like to thank Dr. Lisa Gandy for her suggestions to improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Haque, K.F., Abdelgawad, A. (2021). Prospects of Internet of Things (IoT) and Machine Learning to Fight Against COVID-19. In: Kanoun, O., Derbel, N. (eds) Advanced Systems for Biomedical Applications. Smart Sensors, Measurement and Instrumentation, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-030-71221-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-71221-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71220-4
Online ISBN: 978-3-030-71221-1
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)