Laser Doppler Vibrometry for detecting survivors in hard-to-reach environments

Abstract

Search and Rescue (SAR) operations in remote and hazardous environments are crucial for the rapid and accurate location of survivors, with a timely response being essential during the “golden hours” following a disaster. Recent technological advancements offer innovative solutions to enhance SAR efforts. This study aims to investigate the use of Laser Doppler Vibrometry (LDV) as a tool for remote vital sign assessment and explore its integration with Machine Learning (ML) techniques for accurate individual identification in challenging SAR scenarios. Various scenarios, such as different distances, difficult angles, and non-ideal body placements, are explored in the study to faithfully recreate hard-to-reach environments. Two models, the OS-Model trained with data acquired under optimal conditions and the AS-Model trained with data acquired including all the different conditions studied, were compared to evaluate classification performance. Results indicate that the LDV-assisted ML approach, particularly the AS-Model, exhibits promising outcomes with a higher median prediction accuracy of 0.93, emphasizing the importance of diverse and comprehensive datasets. However, limitations regarding accuracy at greater distances, smaller angles, and lower-body laser targeting must be considered for practical implementation.