Abstract
In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient’s consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims’ mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies’ longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.
Highlights
Disaster medicine frequently is characterized by lack of personal resources where an efficient and keen triage is essential for rescuing survivors
In the reference frame, the IRT or RGB frame is preprocessed to have the most mask R-convolutional neural network (CNN) model has three channels (all feature points dispersed in the region of interest (ROI), which is the human body
An algorithm was proposed that proved capable of detecting human body immobility through visible and infrared technologies in an outside environment using static cameras
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Disaster medicine frequently is characterized by lack of personal resources where an efficient and keen triage is essential for rescuing survivors. Individuals are categorized according to injury severity and available medical resources [1]. Medical responders are overwhelmed and rationing healthcare resources is inevitable [1]. The focus is not on individuals but on populations, and these approaches’
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