A Review on Machine Learning Models for Breathing Pattern Analysis of Soldiers

Since 2001, the U.S. military has sent 2.7 million people to support missions in Afghanistan and Asia. The experience of land-based employees is increased by exposure to additional inhalational exposures and particulate matter from a variety of sources. For the purpose of preventing significant loss to the nation and to the individual military soldier, post-traumatic stress disorder (PTSD) must be identified. Breathing pattern analysis is a key method for detecting PTSD, and various studies have used machine learning techniques for this purpose. This survey examines multiple ML models to determine the military soldiers' breathing patterns in distinct works. This overview discusses several ML models and strategies used over the past few decades for conducting extensive research. Military personnel' breathing patterns are analyzed using a variety of datasets, statistical factors, and methodologies. The effectiveness of various algorithms is compared using qualitative as well as quantitative approaches. The potential future study areas with major challenges are discussed to reach a conclusion.