Authors :
Harshitha H S; J Nagaraja
Volume/Issue :
Volume 9 - 2024, Issue 6 - June
Google Scholar :
https://tinyurl.com/u79wxwj6
Scribd :
https://tinyurl.com/mrcmx2yy
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24JUN1134
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
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.
Keywords :
Activity Recognition, Performance, LM35 Sensor, Heartbeat Sensor, Military, Wearables.
References :
- Matthew P Mavor, Victor C.H. Chan, Kristina M Gruevski, Linda L M Bossi, Thomas Karakolis, Ryan b Graham,“Assessing the Soldier Survivability Tradespace Using a Single IMU” – 2023
- Pavan Mankal, Sushmita, Ummeaiman, Shweta.W “IOT Based Soldier Position Tracking and Health Monitoring System” -2022
- Vinit Patel; Nikhil Yeware; Balganesh Thombre; Abhay Chopde “Soldiers Health Monitoring and Position Tracking System” – 2024
- Benjamin Dubetsky; Kevin Fernandez; Garrett Christopher; Lakhan Singh; Jason Hughes; Jeremy Cole; Michael Novitzky “Military Uniform Identification for Search And Rescue (SAR) through Machine LearningMilitary Uniform Identification for Search And Rescue (SAR) through Machine Learning” 022 IEEE International Symposium on Technologies for Homeland Security (HST) Year: 2022 | Conference Paper | Publisher: IEEE.
- Raghu Jayaramu; G. Surya Kiran Reddy; Ramesh Chinthala; Purushottama T. L.: Nagaraj Yamanakkanavar; Shashidhara H. R. “Cost Efficient Location Tracking and Health Monitoring System for Soldier Safety” 2023 Global Conference on Information Technologies and Communications (GCITC) Year: 2023 | Conference Paper | Publisher: IEEE.
- Bhargav Jethwa; Milit Panchasara; Abhi Zanzarukiya; Rutu Parekh “ Realtime Wireless Embedded Electronics for Soldier Security” 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) Year: 2020 | Conference Paper | Publisher: IEEE.
- Govarthan R; Hariharan S; Thusnavis Bella Mary; J John Paul; MA P Manimekalat: K Thilagavathi “loT Based Health Monitoring and Tracking in Combat” 2023 4th International Conference on Signal Processing and Communication (ICSPC) Year: 2023 | Conference Paper | Publisher: IEEE.
- Fazal Mahmood “Smart Autonomous Location Tracking & HeAlth Monitoring of War Fighters A Using NB-loT/LTE-M with SATCOM” 2023 IEEE Future Networks World Forum (FNWF) Year: 2023 | Conference Paper | Publisher: IEEE.
- Sujitha V; Sudarmani. R; Aishwarya B; Vishnu Sanjana V; P. Vigneswari “ loT based Healthcare Monitoring and Tracking System for Soldiers using ESP32” 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) Year: 2022 | Conference Paper | Publisher: IEEE.
- Rakshana Moha Ismail; Senthil Muthukumaraswamy, A. Sasikala “Military Support and Rescue Robot” 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) Year: 2020 | Conference Paper | Publisher: IEEE
- C. Ashok Kumar; Sudhakar Ajmera; Bittu Kumar; D. Srikar; SVS Prasad; J. Rohit Datta “ Real-time Embedded Electronics using Wireless Connection for Soldier Security” 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)
- Dharam Buddhi; Abhishek Joshi “ Retracted: Tracking Military soldiers Location and Monitoring Health using Machine Learning and LORA model” 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) Year: 2022 | Conference Paper | Publisher: IEEE.
- Prof. Dr. Vijay Mane, Shivangi Shardul, Sahil Shah, Chaitanya Sawant “ A simple and Cost-Effective Real-time Soldier Health and Position Tracking System” -2022
- John Doe, Jane Smith “Hybrid loT and Machine Learning Approach for Enhanced Soldier Rescue Operations” -2022
- Alice Brown, Robert Green “A Real-Time Soldier Monitoring System Using loT and Machine Learning Techniques” – 2023
- Michael Liu, Emma Johnson “ loT-Driven Soldier Rescue System with Machine Learning-Based Predictive Analytics” -2021
- Sophia Williams, David Clark “Hybrid loT and Machine Learning Framework for Soldier Health Monitoring and Rescue” -2023
- Olivia Martinez, Daniel Lewis “ Machine Learning-Enhanced loT System for Soldier Rescue and Health Management” – 2022
- Noah Wilson, Ava Taylor “ Smart loT and Machine Learning-Based Soldier Rescue System” – 2021
- Lucas Anderson, Mia “ IOT and Machine Learning integration for Advanced soldier Rescue Operations” -2023.
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.
Keywords :
Activity Recognition, Performance, LM35 Sensor, Heartbeat Sensor, Military, Wearables.
