Remote Health Parameter Monitoring Using Internet of Things: An Edge-Cloud Centric Integration for Real-time Reporting
DOI:
https://doi.org/10.59461/ijdiic.v4i1.163Keywords:
Edge Cloud, Internet of things, Remote health monitoring , Real-time Health Data, Wearable sensorsAbstract
Monitoring human health has become a phenomenon that integrates cutting-edge technology, which is capable of provisioning updated and sufficient data information to support human well-being in general. A key component of healthy living is preventing disease and health issues in general. The development of Internet of Things (IoT) technology has greatly improved a number of industries, including healthcare. In this study, a unique four-layered architecture for a Remote Health Parameter Monitoring (RHPM) system that uses sensors to monitor blood oxygen concentration (SpO2), body temperature (BT), and heart rate (HR) is presented. The system incorporates edge and cloud computing technologies. Data preprocessing at the edge is performed using an Arduino ESP8266 board and transmitted to cloud servers via the Message Queuing Telemetry Transport protocol for real-time processing and visualization. The testing of the system returned very high accuracy, yielding Mean Absolute Percentage Error (MAPE) values of 2.32%, 2.94%, and 3.43% for BT, SpO2, and HR, respectively. Another metric of evaluation was the R-squared value, which yielded 98% for BT and 97% for both SpO2 and HR, respectively. This paper integrates Support Vector Machine models, which enhances its predictive capability and achieves a cross-validation accuracy of 94.7%. The result indicated that the RHPM system is able to improve the well-being of the patient through early detection and informed preventive health management. Health institutions can tap into the real-time characteristics of the health parameters being monitored in fueling medical decision support systems for improved customer satisfaction and the delivery of modern healthcare solutions.
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C. Obasi, I. Ndu, and O. Iloanusi, “A Framework for Internet of Things-Based Body Mass Index Estimation and Obesity Prediction,” in 2020 International Conference on e-Health and Bioengineering (EHB), IEEE, Oct. 2020, pp. 1–4. doi: 10.1109/EHB50910.2020.9280202.
Adnene Arbi and Mohammad Israr, “Empowering Cyber-Physical Systems through AI-driven Fusion for Enhanced Health Assessment,” Int. J. Data Informatics Intell. Comput., vol. 3, no. 3, pp. 16–23, Aug. 2024, doi: 10.59461/ijdiic.v3i3.127.
J. X. and L. Xu, “Sensor System and Health Monitoring,” Integr. Syst. Heal. Manag., 2017.
L. M. S. do Nascimento, L. V. Bonfati, M. L. B. Freitas, J. J. A. Mendes Junior, H. V. Siqueira, and S. L. Stevan, “Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review,” Sensors, vol. 20, no. 15, p. 4063, Jul. 2020, doi: 10.3390/s20154063.
WHO, “Health Monitoring Service,” World Heal. Organ., 2023, [Online]. Available: https://www.who.int/teams/integrated-health-services/monitoring-health-services
T. Malche et al., “Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring,” J. Healthc. Eng., vol. 2022, pp. 1–15, Mar. 2022, doi: 10.1155/2022/8732213.
Atta Ur Rahman, Bibi Saqia, Yousef S. Alsenani, and Inam Ullah, “Data Quality, Bias, and Strategic Challenges in Reinforcement Learning for Healthcare: A Survey,” Int. J. Data Informatics Intell. Comput., vol. 3, no. 3, pp. 24–42, Sep. 2024, doi: 10.59461/ijdiic.v3i3.128.
O. Hochberg and I. Berger, “Bedside EEG Monitoring in the Neonatal Intensive Care Unit,” Curr. Treat. Options Pediatr., vol. 8, no. 3, pp. 295–307, May 2022, doi: 10.1007/s40746-022-00248-9.
N. Y. Philip, J. J. P. C. Rodrigues, H. Wang, S. J. Fong, and J. Chen, “Internet of Things for In-Home Health Monitoring Systems: Current Advances, Challenges and Future Directions,” IEEE J. Sel. Areas Commun., vol. 39, no. 2, pp. 300–310, Feb. 2021, doi: 10.1109/JSAC.2020.3042421.
M. Dadkhah, M. Mehraeen, F. Rahimnia, and K. Kimiafar, “Use of Internet of Things for Chronic Disease Management,” J. Med. Signals Sensors, vol. 11, no. 2, pp. 138–157, Apr. 2021, doi: 10.4103/jmss.JMSS_13_20.
S. Majumder, T. Mondal, and M. Deen, “Wearable Sensors for Remote Health Monitoring,” Sensors, vol. 17, no. 1, p. 130, Jan. 2017, doi: 10.3390/s17010130.
A. Ray and H. Ray, “Wearable Sensors based Smart Secured Remote Health Monitoring System,” in 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), IEEE, Feb. 2021, pp. 1–6. doi: 10.1109/ICAECT49130.2021.9392533.
A. Gutte and R. Vadali, “IoT Based Health Monitoring System Using Raspberry Pi,” in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), IEEE, Aug. 2018, pp. 1–5. doi: 10.1109/ICCUBEA.2018.8697681.
A. Kaur and A. Jasuja, “Health monitoring based on IoT using Raspberry PI,” in 2017 International Conference on Computing, Communication and Automation (ICCCA), IEEE, May 2017, pp. 1335–1340. doi: 10.1109/CCAA.2017.8230004.
S. K, S. K, Y. M. G, and T. P, “Smart Health Monitoring System for Coma Patients using IoT,” in 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), IEEE, Feb. 2023, pp. 1342–1347. doi: 10.1109/ICCMC56507.2023.10084196.
H. L. Yimer, H. D. Degefa, M. Cristani, and F. Cunico, “IoT-Based Coma Patient Monitoring System,” Nov. 2024, [Online]. Available: http://arxiv.org/abs/2411.13345
R. Subha, M. Haritha, B. Nithishna, and S. G. Monisha, “Coma Patient Health Monitoring System Using IOT,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Mar. 2020, pp. 1454–1457. doi: 10.1109/ICACCS48705.2020.9074174.
Ramesh Saha, S. Biswas, S. Sarmah, S. Karmakar, and P. Das, “A Working Prototype Using DS18B20 Temperature Sensor and Arduino for Health Monitoring,” SN Comput. Sci., vol. 2, no. 1, p. 33, Feb. 2021, doi: 10.1007/s42979-020-00434-2.
S. Ramadurgam and D. G. Perera, “An Efficient FPGA-Based Hardware Accelerator for Convex Optimization-Based SVM Classifier for Machine Learning on Embedded Platforms,” Electronics, vol. 10, no. 11, p. 1323, May 2021, doi: 10.3390/electronics10111323.
B. A. Ikharo and D. Aliu, “Challenges Associated with Wearable Internet-of-Things (IoTs) Monitoring Systems for E-Health,” FUOYE J. Eng. Technol., vol. 8, no. 4, Dec. 2023, doi: 10.46792/fuoyejet.v8i4.1099.
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