Empowering Healthcare Transformation Through IoT and Big Data Integration in Remote Real-time Patient Monitoring
Keywords:
Remote Real-time Patient Monitoring, Asthma, Heart Failure, Internet of Things, Big Data Management and Analytics, Healthcare, Machine Learning, Artificial Intelligence, Data Privacy, SecurityAbstract
The healthcare landscape is undergoing a significant transformation driven by the convergence of advanced technologies like the Internet of Things (IoT), big data, and artificial intelligence (AI). Remote real-time patient monitoring with IoT-based big data management and analytics emerges as a revolutionary paradigm, promising to redefine how we monitor and manage patient health. Remote Real-time Patient Monitoring (RRPM) has emerged as a transformative force in healthcare, particularly for chronic conditions like asthma seizures and heart failures. This paper explores the integration of RRPM systems with the Internet of Things (IoT) and Big Data technologies to revolutionize patient care. Using asthma and heart failure as case studies, we delve into the functionalities of RRPM systems, highlighting their ability to continuously collect and transmit vital signs, detect early warning signs of exacerbations, and facilitate proactive interventions. We then delve into the crucial role of IoT-based Big Data Management and Analytics (BDMA) in RRPM. This paper examines the challenges and opportunities presented by BDMA in healthcare, focusing on data acquisition, storage, analysis, and visualization. We analyze how advanced analytics like machine learning and artificial intelligence can enable predictive modeling, personalized care plans, and real-time decision support for healthcare professionals. Finally, we address the ethical and regulatory considerations surrounding patient data privacy and security within RRPM systems.
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S.M.R. Islam, D. Kwak, M.H. Kabir, et al. The internet of things for health care: a comprehensive survey, IEEE Access, 3 (2015), pp. 678-708, 10.1109/ACCESS.2015.2437951.
B. Talbot, et al. Patient and clinician perspectives on the use of remote patient monitoring in peritoneal dialysis, Can. J. Kidney Heal. Dis., 9 (2022).
T. Malche, et al. Artificial intelligence of things- (AIoT-) based patient activity tracking system for remote patient monitoring, J. Healthc. Eng., 2022 (2022).
H. Fouad, A.S. Hassanein, A.M. Soliman, H. Al-Feel. Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction, Meas. J. Int. Meas. Confed., 159 (2020), Article 107757.
Alshamrani, T., Alswayri, M., Alamri, A., Alzahrani, M., & Alyahyan, E. (2023). IoT based real time patient monitoring and analysis using Raspberry Pi 3. Research Gate.
Bhardwaj, R., Garg, A., & Mittal, S. (2021). Exploring real-time patient monitoring and data analytics with IoT-based smart healthcare monitoring. Research Gate.
Char, D. S., Shah, N. H., Magnus, D., & Schuessler, K. (2021). Implementing a fair and ethical artificial intelligence in healthcare. Nature medicine, 27(12), 2089-2094.
Chen, M., Yang, Y., Sun, P., Li, B., & Khan, A. (2020). Blockchain-based healthcare service network with fog computing for privacy-preserving and secure e-health records. Journal of medical systems, 44(7), 1-13.
Fang, Y., Ding, J., Wu, D., Liu, J., & Zhu, H. (2022). Enabling interoperable healthcare services in 5G-based medical internet of things: A lightweight middleware approach. IEEE Internet of Things Journal, 9(8), 7054-7067.
Vahedi, M., Jabbari, B., Faghani, F., & Alizadeh, M. (2020). A novel IoT-based platform for remote patient monitoring during COVID-19 pandemic. Journal of Medical Systems, 44(11), 174.
Yu, R., Zhang, Y., Lin, Y., & Huang, J. (2022). Wearable sensor-based real-time depression monitoring system using machine learning. IEEE Transactions on Affective Computing.
Ozdemir, A. T., & Akan, Ö. (2020). A novel IoT-based anxiety monitoring system for mental health. Computers & Electrical Engineering, 88, 106765..
Al-Ani et al. (2021). "Remote Mental Health Monitoring Using Wearable Sensors and Machine Learning: A Review." Sensors 21(21):7260.
Martinez-Garcia et al. (2022). "A Review of Wearable Technology for Mental Health Monitoring in Older Adults." Sensors 22(14):5608.
Nguyen et al. (2023). "Deep Learning for Mental Health: A Review." arXiv preprint arXiv:2301.08510.
Jiang et al. (2022). "Artificial Intelligence in Healthcare: Past, Present and Future." Stroke and Vascular Neurology 7(2):230-243.
Luo et al. (2023). "Big Data Analytics and Artificial Intelligence in Healthcare: A Survey." IEEE Journal of Biomedical and Health Informatics 27(6):1201-1217.
Chowdhury et al. (2020). "The Role of Big Data Analytics in Healthcare: A Survey." arXiv preprint arXiv:2011.05602.
Ray et al. (2022). "Ethical Considerations in Artificial Intelligence for Healthcare." Nature Biomedical Engineering 6(9):735-745.
Cirani S, Picone M. Wearable computing for the internet of things. IT Prof. 2015;17(5):35–41.
Guberović, E., T. Lipić, and I. Čavrak. Dew intelligence: feder- ated learning perspective. in 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). 2021.
do Nascimento LM, et al. Sensors and systems for physi- cal rehabilitation and health monitoring—A review. Sensors. 2020;20(15):4063.
Li X, et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 2017;15(1):e2001402.
Bahmani A, et al. A scalable, secure, and interoperable plat- form for deep data-driven health management. Nat Commun. 2021;12(1):5757.
Anuar, H. and P.L. Leow. Non-invasive core body temperature sensor for continuous monitoring. in 2019 IEEE International Conference on Sensors and Nanotechnology. 2019.
Huang, P., et al. An embedded non-contact body temperature measurement system with automatic face tracking and neural network regression. in 2016 International Automatic Control Conference (CACS). 2016.
Rahaman A, et al. Developing IoT based smart health monitoring systems: a review. Rev d’Intell Artif. 2019;33(6):435–40.
Huang M, et al. A wearable thermometry for core body tem- perature measurement and its experimental verification. IEEE J Biomed Health Inform. 2017;21(3):708–14.
Albahri AS, et al. IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art. J Netw Comput Appl. 2021;173:102873.
Hong-tan L, et al. Big data and ambient intelligence in IoT-based wireless student health monitoring system. Aggress Violent Behav. 2021. https://doi.org/10.1016/j.avb.2021.101601.
Sharma N, et al. A smart ontology-based IoT framework for remote patient monitoring. Biomed Signal Process Control. 2021;68:102717.
Al Bassam N, et al. IoT based wearable device to monitor the signs of quarantined remote patients of COVID-19. Inform Med Unlocked. 2021;24:100588.
Paganelli AI, et al. A conceptual IoT-based early-warning archi- tecture for remote monitoring of COVID-19 patients in wards and at home. Internet Things. 2021. https://doi.org/10.1016/j.iot.2021. 100399.
Moghadas E, Rezazadeh J, Farahbakhsh R. An IoT patient moni- toring based on fog computing and data mining: cardiac arrhyth- mia usecase. Internet Things. 2020;11:100251.
Akhbarifar S, et al. A secure remote health monitor- ing model for early disease diagnosis in cloud-based IoT
environment. Pers Ubiquit Comput. 2020. https://doi.org/10.1007/ s00779-020-01475-3.
Alhussein M, et al. Cognitive IoT-cloud integration for smart healthcare: case study for epileptic seizure detection and moni- toring. Mobile Netw Appl. 2018;23(6):1624–35.
Poongodi M, et al. Smart healthcare in smart cities: wire- less patient monitoring system using IoT. J Supercomput. 2021;7(11):12230–55.
Wan J, et al. Wearable IoT enabled real-time health monitoring system. EURASIP J Wirel Commun Netw. 2018;2018(1):298.
Uslu BÇ, Okay E, Dursun E. Analysis of factors affecting IoT- based smart hospital design. J Cloud Comput. 2020;9(1):67.
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