Journal of Angiotherapy
Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
  1. You are here:
  2. Home
  3. Journals
  4. Biological Sciences
RESEARCH ARTICLE   (Open Access)
Contents Vol 8 (7)

Wireless Body Sensor Networks for Real-Time Healthcare Monitoring: A Cost-Effective and Energy-Efficient Approach

Yuvaraja M 1*, R. Ramesh 2, R. Priya 3, J. Dhanasekar 4

+ Author Affiliations

Journal of Angiotherapy 8 (7) 1-13 https://doi.org/10.25163/angiotherapy.879796

Submitted: 18 May 2024 Revised: 15 July 2024  Published: 22 July 2024 

Abstract

Background: The continuous and accurate monitoring of physiological signals is critical for timely medical interventions, particularly in the context of chronic disease management and elderly care. The advent of Wireless Body Sensor Networks (WBSN) and Patient Health Monitoring Systems (PHMS) promises to revolutionize healthcare by integrating real-time data collection with advanced processing and communication technologies, enabling proactive medical responses through the Internet of Things (IoT). Methods: In this study, a WBSN-PHMS framework is proposed, leveraging a network of biomedical sensors to continuously monitor vital signs such as temperature and heart rate. The system includes a gateway node that wirelessly transmits sensor data to a cloud-based storage and processing system. Physiological data are processed in real-time using advanced algorithms to detect anomalies and trigger alerts. The health status is modeled using a function g(v,w(v),∅−1(v(w))−∂i(v,∂))g(v, w(v), \varnothing^{-1}(v(w)) - \partial_i(v, \partial))g(v,w(v),∅−1(v(w))−∂i?(v,∂)), which integrates patient-specific factors and real-time physiological states. The processed data are communicated efficiently through IoT platforms, ensuring that healthcare professionals receive timely alerts via mobile applications and specialized dashboards. Results: The WBSN-PHMS demonstrated high accuracy in detecting abnormal temperature and heart rate patterns, achieving detection rates of 98.61% and 99.43%, respectively. Additionally, the system improved overall patient health monitoring by 98.42%, offering a significant advancement in patient safety and healthcare efficiency. The cost-effectiveness of the system was established with a 96.21% improvement, while energy consumption was reduced by 23%, underscoring the sustainability of the technology. Conclusion: The WBSN-PHMS proves to be a reliable and efficient healthcare monitoring system that enhances patient outcomes through real-time data analysis and rapid medical responses. The system's ability to continuously monitor and analyze physiological signals in a non-intrusive manner positions it as a vital tool in modern medical practice, with the potential to revolutionize remote patient care and monitoring.

Keywords: Wireless Body Sensor Networks (WBSN), Healthcare Monitoring, Internet of Things (IoT), Energy Efficiency, Real-Time Data Transmission

 

References

Abedi, A. F. A., Goh, P., & Alkhayyat, A. (2024). Nano-Sensors Communications and Networking for Healthcare Systems: Review and Outlooks. Journal of Computational Science, 102367. https://doi.org/10.1016/j.jocs.2024.102367

Adarsh, A., & Kumar, B. (2020). Wireless medical sensor networks for smart e-healthcare. In Intelligent Data Security Solutions for e-Health Applications (pp. 275-292). https://doi.org/10.1016/B978-0-12-819511-6.00015-7

Adeniyi, E. A., Ogundokun, R. O., & Awotunde, J. B. (2021). IoMT-based wearable body sensors network healthcare monitoring system. IoT in healthcare and ambient assisted living, 103-121. https://doi.org/10.1007/978-981-15-9897-5_6

Ahmad, N., Awan, M. D., Khiyal, M. S. H., Babar, M. I., Abdelmaboud, A., Ibrahim, H. A., & Hamed, N. O. (2022). Improved QoS aware routing protocol (IM-QRP) for WBAN based healthcare monitoring system. IEEE Access, 10, 121864-121885. https://doi.org/10.1109/ACCESS.2022.3223085

Ali, F., El-Sappagh, S., Islam, S. R., Ali, A., Attique, M., Imran, M., & Kwak, K. S. (2021). An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generation Computer Systems, 114, 23-43. https://doi.org/10.1016/j.future.2020.07.047

Bedi, P., Goyal, S. B., Sharma, R., Yadav, D. K., & Sharma, M. (2021). Smart model for big data classification using deep learning in wireless body area networks. In Micro-Electronics and Telecommunication Engineering: Proceedings of 4th ICMETE 2020, pp. 215-224. https://doi.org/10.1007/978-981-33-4687-1_21

Behura, A., Sahu, S., & Kabat, M. R. (2021). Advancement of Machine Learning and cloud computing in the field of Smart Health Care. Machine Learning Approach for Cloud Data Analytics in IoT, 273-306. https://doi.org/10.1002/9781119785873.ch11

Bhandari, K. S., Seo, C., & Cho, G. H. (2020, September). Towards Sensor-Cloud Based Efficient Smart Healthcare Monitoring Framework using Machine Learning. In The 9th International Conference on Smart Media and Applications, pp. 380-383. https://doi.org/10.1145/3426020.3426138

Chakraborty, C., & Kishor, A. (2022). Real-time cloud-based patient-centric monitoring using computational health systems. IEEE transactions on computational social systems, 9(6), 1613-1623. https://doi.org/10.1109/TCSS.2022.3170375

Chakraborty, S., Mali, K., & Chatterjee, S. (2021). Edge computing based conceptual framework for smart health care applications using z-wave and homebased wireless sensor network. Mobile Edge Computing, 387-414. https://doi.org/10.1007/978-3-030-69893-5_16

Chandra, S., Chandra, A., & Gupta, R. (2021). An efficient data routing scheme for multi-patient monitoring in a biomedical sensor network through energy equalization strategy. Wireless Networks, 27(1), 635-648. https://doi.org/10.1007/s11276-020-02472-3

Dewangan, N., Vyas, P., & Mandal, S. (2022). Smart healthcare and intelligent medical systems. In Computational Intelligence and Applications for Pandemics and Healthcare, pp. 205-228. https://doi.org/ 10.4018/978-1-7998-9831-3.ch010

El Attaoui, A., Largo, S., Jilbab, A., & Bourouhou, A. (2021). Wireless medical sensor network for blood pressure monitoring based on machine learning for real-time data classification. Journal of Ambient Intelligence and Humanized Computing, 12(9), 8777-8792. https://doi.org/10.1007/s12652-020-02660-1

Ettyem, S. A., Ahmed, I., Ahmed, W. S., Hussien, N. A., Majeed, M. G., Cengiz, K., & Benameur, N. (2023). Intelligent Wireless Sensor Networks for Healthcare: Bridging Biomedical Clothing to the IoT Future. Journal of Intelligent Systems & Internet of Things, 9(2). https://doi.org/ 10.54216/JISIoT.090203

https://www.kaggle.com/datasets/hankyujang/healthcare-personnel-movement-data

Ivanov, M., Markova, V., & Ganchev, T. (2020, September). An overview of network architectures and technology for wearable sensor-based health monitoring systems. In 2020 International Conference on Biomedical Innovations and Applications (BIA), pp. 81-84. https://doi.org/10.1109/BIA50171.2020.9244286

Jain, P., Panesar, S. F., Talwar, B. F., & Sah, M. K. (2021). IoT-Based Solutions for Smart Healthcare. Emerging Technologies for Healthcare: Internet of Things and Deep Learning Models, 25-67. https://doi.org/10.1002/9781119792345.ch2

JARIAL, R., DUBEY, A., & DUBEY, A. (2024). Real-Time Health Monitoring Using IoT Sensors. Advanced Research in Electronic Devices for Biomedical and Health, 181.

Lamonaca, F., Carnì, D. L., & Scuro, C. (2021, October). Synchronization of Wireless Sensor Networks for Biomedical Measurement Systems. In 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), pp. 325-328. https://doi.org/10.1109/TELSIKS52058.2021.9606332

Nayak, M., & Barman, A. (2022). A real-time cloud-based healthcare monitoring system. In Computational Intelligence and Applications for Pandemics and Healthcare, pp. 229-247. https://doi.org/ 10.4018/978-1-7998-9831-3.ch011

Rajan Jeyaraj, P., & Nadar, E. R. S. (2022). Smart-monitor: Patient monitoring system for IoT-based healthcare system using deep learning. IETE Journal of Research, 68(2), 1435-1442. https://doi.org/10.1080/03772063.2019.1649215

Salunke, G. D., Singh, V. P., & Jadhav, C. R. (2022). A Review Approach to Modeling, Analysis, and Design Framework for Wireless Sensor Network Health Monitoring Systems. Journal of Pharmaceutical Negative Results, 165-177.https://doi.org/ 10.47750/pnr.2022.13. S02.24

Subasini, C. A., Karuppiah, S. P., Sheeba, A., & Padmakala, S. (2021). Developing an attack detection framework for wireless sensor network-based healthcare applications using hybrid convolutional neural network. Transactions on Emerging Telecommunications Technologies, 32(11), 4336. https://doi.org/10.1002/ett.4336

Uddin, R., & Koo, I. (2024). Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity. Applied Sciences, 14(5), 1876. https://doi.org/10.3390/app14051876

Vistro, D. M., Munawar, A., Iftikhar, A., Qasim, A., & Rehman, A. U. (2020). Tertiary care hospital monitoring system using wireless sensors. Journal of Critical Reviews, 7(10), 1504-1511. http://dx.doi.org/10.31838/jcr.07.10.281

Zhao, J., & Li, G. (2020). Study on real-time wearable sport health device based on body sensor networks. Computer Communications, 154, 40-47. https://doi.org/10.1016/j.comcom.2020.02.045

PDF
Abstract
Export Citation

View Dimensions


View Plumx


View Altmetric




Save
0
Citation
658
View

Share