Detection and classification of human respiration under building debris model using VHF/UHF waves

Abstract

AbstractDetecting live humans in buildings that have collapsed due to disasters and identifying their condition of health is of great importance for search and rescue operations. Although various methods have been used for this purpose, there are still critical challenges to ensure accurate and rapid life-saving operations. Immediate detection of the presence of living humans under debris combined with the assessment of their vital signs is a crucial factor. This research endeavors to introduce a previously unexplored method: the use of artificial neural network-based techniques to detect human respiration under building debris by generating novel simulation-derived electromagnetic data. To achieve this, a realistic three-dimensional debris model was integrated into an electromagnetic simulation program and complemented by a phantom simulating anterior–posterior body movements indicative of respiration. Measurements of magnitude and phase between 150 and 650 MHz were performed under different conditions. Using surrogate models based on artificial neural networks, noise with different signal-to-noise ratios within the selected frequencies was introduced. These models were used to perform two different steps. Firstly, the presence of respiration of living humans trapped under debris was successfully detected with a success rate of 99.97%. Secondly, the difficult task of classifying the respiration patterns of the human was accomplished with an impressive accuracy of 99.69%, providing a solid basis for proof of concept.