Hybrid Approach Using Machine Learning and IOT for Soldier Rescue : A Review

Abstract

Despite the well-established influence of various factors on a soldier's burden – including environment, physical exertion, equipment design, and mental stress – our current understanding is largely based on studies conducted in controlled lab settings, focusing solely on the weight of carried equipment. This limited scope hinders a comprehensive picture of how these combined burdens impact a soldier's ability to survive on the battlefield, encompassing factors like performance, health, and vulnerability to enemy attacks. To bridge this gap and gain a more holistic understanding, field-based methods for capturing soldier movement are crucial. In this vein, we've developed a novel human activity recognition system. Trained using data collected from a single sensor placed on a soldier's upper back, the system can identify eleven distinct tactical movement patterns commonly employed by soldiers in the field. This advancement paves the way for a more nuanced understanding of how various burden factors interact and influence a soldier's effectiveness and safety in real-world scenarios. Using K- Nearest Neighbour, SVM Classifier, Logistic Regression, Naïve Bayer algorithms real-world constraints are forced, and class labels are expanded. This project is based on health monitoring and tracking system for soldiers. The proposed system can be mounted on the soldier’s jacket to track their health status and current locating using GPS. This information will be transmitted to the control room through IOT and ML. The proposed system comprises of tiny wearable physiological equipment’s, sensors, transmission modules. Hence, with the use of the proposed equipment, it is possible to implement a low-cost mechanism to protect the valuable human life on the battlefield. It also includes about securing of data of soldiers in the cloud. This new method offers a powerful tool for military leaders and scientists. By collecting real-world data on soldier burden, it allows them to quantify the complex factors affecting soldier performance (the tradespace). This data acts as valuable pre-processing for other technologies, ultimately enabling data-driven decisions to optimize soldier well-being, minimize risk, and maximize mission success.