Abstract
At present, the novel coronavirus is posing a bigger threat to the whole human race. However, adopting emerging technologies like the Internet of Things, artificial intelligence, blockchain technology, and machine learning helps in a greater deal to handle the pandemic effectively. The virus has cost many human lives, and one primary reason could be neglecting the symptoms. The earlier identification of the disease will help in providing immediate medical support. Wearable devices are effective in identifying the coronavirus disease, i.e., COVID-19 cases than the traditional methods. They help in improved tracking and management of the disease. This paper intends to present a framework where the wearable devices associated with the Internet of Things (IoT) collect information from individuals and store it either in a cloud server or to a local fog computing node. Furthermore, the implementation of a decentralized storage system to collect, analyze, and preserve the gathered data among the healthcare professionals is also included in the system. The system takes advantage of blockchain systems to automate smart contracts. The goal of this research is to see how wearable gadgets might help combat COVID-19’s effects by providing efficient methods for identification, monitoring, and collaboration in remote patient monitoring.
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References
Hirten, R., Danieletto, M., Tomalin, L., Choi, K., Zweig, M., Golden, E., Kaur, S., Helmus, D., Biello, A., Pyzik, R., Charney, A., Miotto, R., Glicksberg, B., Levin, M., Nabeel, I., Aberg, J., Reich, D., Charney, D., Bottinger, E., Keefer, L., Suarez-Farinas, M., Nadkarni, G., Fayad, Z.: Use of physiological data from a wearable device to identify SARS-CoV-2 infection and symptoms and predict COVID-19 diagnosis: observational study. J. Med. Internet Res. 23(2), e26107 (2021). https://www.jmir.org/2021/2/e26107. https://doi.org/10.2196/26107
Angelov, G.V., Nikolakov, D.P., Ruskova, I.N., Gieva, E.E., Spasova, M.L.: Healthcare sensing and monitoring. In: Ganchev, I., Garcia, N., Dobre, C., Mavromoustakis, C., Goleva, R. (eds.) Enhanced Living Environments. Lecture Notes in Computer Science, vol. 11369. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10752-9_10
Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., Zomaya, A.Y.: Edge intelligence: the confluence of edge computing and artificial intelligence. IEEE Internet Things J. 7, 7457–7469 (2020)
Kern, Nicky, G. Tröster, B. Schiele, H. Junker and P. Lukowicz. “Wearable Sensing to Annotate Meeting Recordings.” SEMWEB (2002).
Rodgers, M., Pai, V., Conroy, R.: Recent advances in wearable sensors for health monitoring. IEEE Sens. J. 15, 3119–3126 (2015). https://doi.org/10.1109/JSEN.2014.2357257
Fraga-Lamas, P., Fernández-Caramés, T.M., Noceda-Davila, D., Díaz-Bouza, M., Vilar-Montesinos, M., Pena-Agras, J.D., Castedo, L.: Enabling automatic event detection for the pipe workshop of the shipyard 4.0. In: Proceedings of the 2017 56th FITCE Congress, pp. 20–27. Madrid, Spain (2017)
Sathya, A.R., Banik, B.G.: A comprehensive study of blockchain services: future of cryptography. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 11(10) (2020). https://doi.org/10.14569/IJACSA.2020.0111037
Zheng, X., Sun, S., Mukkamala, R.R., Vatrapu, R., Ordieres-Mere, J.: Accelerating health data sharing: a solution based on the internet of things and distributed ledger technologies. J. Med. Internet Res. 21(6), e13583 (2019). [Online]. Available: https://www.jmir.org/2019/6/e13583/
Chattu, V.K., Nanda, A., Chattu, S.K., Kadri, S.M., Knight, A.W.: The emerging role of blockchain technology applications in routine disease surveillance systems to strengthen global health security. Big Data Cogn. Comput. 3(2) (2019). [Online]. Available: https://www.mdpi.com/2504-2289/3/2/25
Griggs, K.N., Ossipova, O., Kohlios, C.P., Baccarini, A.N., Howson, E.A., Haya-jneh, T.: Healthcare blockchain system using smart contracts for secure automatedremote patient monitoring. J. Med. Syst. 42(7), 130 (2018)
Trusted Remote Patient Monitoring using Blockchain-based Smart Contracts. Available from: https://www.researchgate.net/publication/334824084_Trusted_Remote_Patient_Monitoring_using_Blockchain-based_Smart_Contracts. Accessed 29 August 2021
Uddin, M., Salem, A., Nam, I., Nadeem, T.: Wearable sensing framework for human activity monitoring. In: Proceedings of the 2015 Workshop on Wearable Systems and Applications, pp. 21–26 (2015)
Zhu, X., Liu, W., Shuang, S., Nair, M., Li, C.-Z.: Intelligent tattoos, patches, and other wearable biosensors. In: Narayan, R.J. (ed.) Medical Biosensors for Point of Care (poc) Applications, pp. 133–150. Woodhead Publishing, Duxford (2017)
Khan, Y., Ostfeld, A.E., Lochner, C.M., Pierre, A., Arias, A.C.: Monitoring of vital signs with flexible and wearable medical devices. Adv. Mater. 28(22), 4373–4395 (2016). https://doi.org/10.1002/adma.201504366. Epub 2016 Feb 12 PMID: 26867696
Mukhopadhyay, S.C.: Wearable sensors for human activity monitoring: a review. IEEE Sens. J. 15, 1321–1330 (2015). https://doi.org/10.1109/JSEN.2014.2370945
Zhao, Y., Cheng, S., Yu, X., Xu, H.: Chinese public’s attention to the COVID-19 epidemic on social media: observational descriptive study. J. Med. Internet Res. 22(5), e18825 (2020). https://www.jmir.org/2020/5/e18825. https://doi.org/10.2196/18825
Benreguia, B., Moumen, H., Merzoug, M.: Tracking COVID-19 by tracking infectious trajectories. IEEE Access 8, 145242–145255 (2020)
Nilashi, M., Asadi, S., Abumalloh, R.A., Samad, S., Ibrahim, O.: Intelligent recommender systems in the COVID-19 outbreak: the case of wearable healthcare devices. J. Soft Comput. Decis. Support Syst. 7(4), 8–12 (2020)
Hirten, R.P., Danieletto, M., Tomalin, L., Choi, K.H., Zweig, M., Golden, E., Kaur, S., Helmus, D., Biello, A., Pyzik, R., Charney, A., Miotto, R., Glicksberg, B.S., Levin, M., Nabeel, I., Aberg, J., Reich, D., Charney, D., Bottinger, E.P., Keefer, L., Suarez-Farinas, M., Nadkarni, G.N., Fayad, Z.A.: Physiological data from a wearable device identifies SARS-CoV-2 infection and symptoms and predicts COVID-19 diagnosis: observational study (Preprint). J. Med. Internet Res. (2020). https://doi.org/10.2196/26107
Mateen, A., Javaid, N., Iqbal, S.: Towards energy efficient routing in blockchainbased underwater WSNs via recovering the void holes. MS thesis, COMSATS University Islamabad (CUI), Islamabad 44000, Pakistan (2019)
Mohammed, M.N., Syamsudin, H., Al-Zubaidi, S., Sairah, A.K., Ramli, R., Yusuf, E.: Novel COVID-19 detection and diagnosis system using IOT based smart helmet. Int. J. Psychosoc. Rehabil. 24(7) (2020)
Zheng, Z., Xie, S., Dai, H.-N., Chen, W., Chen, X., Weng, J., Imran, M.: An overview on smart contracts: challenges, advances and platforms. Futur. Gener. Comput. Syst. 105, 475–491 (2020)
Peralta, G., Iglesias-Urkia, M., Barcelo, M., Gomez, R., Moran, A., Bilbao, J.: Fog computing based efficient IoT scheme for the Industry 4.0. In: Proceedings of the IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), Donostia, Spain, 24–26 May 2017
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. Helsinki, Finland, 17 August 2012
Markakis, E.K., Karras, K., Zotos, N., Sideris, A., Moysiadis, T., Corsaro, A., Alexiou, G., Skianis, C., Mastorakis, G., Mavromoustakis, C.X., et al.: EXEGESIS: extreme edge resource harvesting for a virtualized fog environment. IEEE Commun. Mag. 55, 7 (2017)
Suárez-Albela, M., Fernández-Caramés, T.M., Fraga-Lamas, P., Castedo, L.: A practical evaluation of a high-security energy-efficient gateway for iot fog computing applications (2017)
Journal, H.: Largest healthcare data breaches of 2018. HIPAA J. (2018)
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Sathya, A.R., Banik, B.G. (2022). A Generic Blockchain-Based Remote Clinical Monitoring Framework Through Wearable Devices to Mitigate the COVID-19 Pandemic. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Intelligent Computing and Applications, Volume 2. Smart Innovation, Systems and Technologies, vol 283. Springer, Singapore. https://doi.org/10.1007/978-981-16-9705-0_42
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