Enhancing Military Healthcare Response with 5G-enabled SVM Health Tracking Systems
DOI:
https://doi.org/10.51459/jostir.2026.2.1.0179Keywords:
Military healthcare, 5G, Wireless sensor networks, Support Vector Machine algorithm, machine learning, health tracking system, health informatics, smart healthcareAbstract
The integration of 5G with emerging technologies is already revolutionizing the healthcare sector. Provisioning enhanced Mobile Broadband (eMBB), ultra-reliable low-latency communication (URLLC), and ubiquitous connectivity, 5G is transforming healthcare by enabling advanced telehealth services, remote monitoring, and high-precision remote procedures. Its high speeds, low latency, and massive connectivity support applications like real-time, high-definition consultations and continuous monitoring of vital signs. 5G technology improves the way crises are handled in battlefield scenarios. Nevertheless, there is a need to improve situational awareness, reduce response times, and enhance survival rates in combat scenarios for the military. The integration of machine learning with a 5G-enabled health tracking system in military contexts would allow to achieve these.
Though traditional hospital-based care provides these and more. However, very unlike traditional hospital-based care, this study focuses on deploying wearable health tracking systems on soldiers, enabling continuous monitoring of vital signs such as heart rate, body temperature, blood pressure, and oxygen saturation while in the battlefield. The collected data will be transmitted in real time via 5G networks and processed using SVM algorithms in MATLAB to detect anomalies and identify soldiers who require urgent medical attention. Thus, in this study, a 5G network health monitoring system based on a machine learning model within a MATLAB simulation environment to enhance military medical response capabilities would be designed, simulated and evaluated.
References
Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A. and Arshad, H., 2018. State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11).
Archana, A., Cinmayee, C. K., Chaithra, E., and Chethan, P. K. D., 2020. Health monitoring and soldier tracking system using IOT. Int J Eng Res Technol, 8, 14.
Armarkar, A. V., Punekar, D. J., Kapse, M. V., Kumari, S., and Shelke, J. A., 2017. Soldier health and position tracking system. International Journal of Engineering Science and Computing, 7(3).
Arri, M. E., 2022. Human Vital Signs Dataset. Kaggle. https://www.kaggle.com/datasets/engrarri21/human-vital-signs
Chakravarth, P., Natarajan, S. and Bennet, M.A., 2017. GSM based soldier tracking system and monitoring using wireless communication. International Journal on Smart Sensing and Intelligent Systems, 10(5), p.259.
Eliyaz, M., Prudvi, M.L.V.S., Reddy, G.P. and Pavan, M., 2020. Soldier Tracking and Health Monitoring System using LabVIEW. International Journal, 8(5).
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M. and Thrun, S., 2017. Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), pp.115-118.
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S. and Dean, J., 2019. A guide to deep learning in healthcare. Nature medicine, 25(1), pp.24-29.
Ginsburg, A. S., Izadnegahdar, R., Francis, D., & Klugman, K. P., 2018. Malaria, tuberculosis, and HIV/AIDS: Leveraging machine learning to optimize diagnosis and management. The Lancet Global Health, 6(8), pp. e858–e859. https://doi.org/10.1016/S2214-109X(18)30224-2
Memon, S., Bibi, S. and He, G., 2025. Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery. Diseases, 13(6), p.184.
Johnson, C. R., Fan, S., & Shi, F., 2018. MATLAB applications in machine learning and healthcare. Computational Intelligence Magazine, 13(3), pp. 38–49.
Jiang, S., 2022. The programming method of MATLAB language for solving AIbased digital health problems. Journal of Commercial Biotechnology, 27(1), pp.151-159.
Khan, A. and RS, D.S., 2022, February. Soldiers Health Monitoring and Position Tracking System. In Proceedings of the International Conference on Innovative Computing & Communication (ICICC).
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M. and Kitai, T., 2017. Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), pp.2657-2664.
Kurhe, P.S. and Agrawal, S.S., 2013. Real time tracking and health monitoring system of remote soldier using ARM 7. International Journal of Engineering Trends and Technology, 4(3), pp.311-315.
LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. nature, 521(7553), pp.436-444.
Lim, H.B., Ma, D., Wang, B., Kalbarczyk, Z., Iyer, R.K. and Watkin, K.L., 2010, June. A soldier health monitoring system for military applications. In 2010 International Conference on Body Sensor Networks (pp. 246-249). IEEE.
McKinney, S.M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G.S., Darzi, A. and Etemadi, M., 2020. International evaluation of an AI system for breast cancer screening. Nature, 577(7788), pp.89-94.
Mdhaffar, A., Chaari, T., Larbi, K., Jmaiel, M. and Freisleben, B., 2017, July. IoT-based health monitoring via LoRaWAN. In IEEE EUROCON 2017-17th international conference on smart technologies (pp. 519-524). IEEE.
Ngwira, C., Mayhew, S.H. and Hutchinson, E., 2021. Community-level integration of health services and community health workers’ agency in Malawi. Social Science & Medicine, 291, p.114463.
Nikam, S., Patil, S., Powar, P. and Bendre, V.S., 2013. GPS based soldier tracking and health indication system. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(3), pp.1082-1088.
Obermeyer, Z. and Emanuel, E.J., 2016. Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine, 375(13), p.1216.
Patel, V., Yeware, N., Thombre, B. and Chopde, A., 2024, February. Soldiers health monitoring and position tracking system. In 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-4). IEEE.
Razzak, M.I., Imran, M. and Xu, G., 2020. Big data analytics for preventive medicine. Neural Computing and Applications, 32(9), pp.4417-4451.
Ross, C., and Anderson, N., 2018. Ethical and legal considerations in the use of machine learning algorithms in health care. AMA Journal of Ethics, 20(11), pp. E1115-1126.
Sailesh, M.P., Kumar, C.V., Cecil, B., Deep, B.M. and Sivraj, P., 2014, July. Smart soldier assistance using WSN. In 2014 International Conference on Embedded Systems (ICES) (pp. 244-249). IEEE.
Singh, G., Hashmi, A., Gaur, V. and Gupta, V., 2023, December. IOT Based Soldier Tracking and Health Indication System. In 2023 9th International Conference on Signal Processing and Communication (ICSC) (pp. 368-373). IEEE.
Ting, D.S., Lin, H., Ruamviboonsuk, P., Wong, T.Y. and Sim, D.A., 2020. Artificial intelligence, the internet of things, and virtual clinics: ophthalmology at the digital translation forefront. The Lancet Digital Health, 2(1), pp.e8-e9.
Tiamiyu, O.A., Akande, H.B. and Abass, F.O., 2024. Analysis and optimization of suburban 5G coverage predictions. FUDMA journal of sciences, 8(5), pp.379-393.
Topol, E.J., 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), pp.44-56.
Wahl, B., Cossy-Gantner, A., Germann, S. and Schwalbe, N.R., 2018. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?. BMJ global health, 3(4), p.e000798.
Walker, W., Aroul, A.P. and Bhatia, D., 2009, September. Mobile health monitoring systems. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5199-5202). IEEE.
Zhou, L., Zhang, Y., Zhang, S., and Wang, L., 2020. 5G: The technology and its applications. IEEE Communications Magazine, 58(6), pp. 10–16. https://doi.org/10.1109/MCOM.001.1900347
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