Enhancing Military Healthcare Response with 5G-enabled SVM Health Tracking Systems - Tiamiyu, Osuolale Abdramon; Yusuff, Nurudeen Omotayo; Ayoola, Gideon Ayomide (Journal of Science, Technology and Innovation Research, 2026)
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.