Feasibility of Live Video Feed Transmission from Unmanned Aerial Vehicles for Medical Surveillance During the 2022 Montreal Marathon.

IF 2.1 4区 医学 Q2 EMERGENCY MEDICINE
Prehospital and Disaster Medicine Pub Date : 2023-10-01 Epub Date: 2023-10-03 DOI:10.1017/S1049023X23006362
Raphaël Lafortune, Eddy Afram, Arielle Grossman, Ann-Rebecca Drolet, François de Champlain, David Iannuzzi, Valérie Homier
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引用次数: 0

Abstract

Introduction: In recent years, unmanned aerial vehicles (UAVs) have been increasingly used for medical surveillance purposes in mass-gathering events. No studies have investigated the reliability of live video transmission from UAVs for accurate identification of distressed race participants in need of medical attention. The aim of this study was to determine the proportion of time during which live medical surveillance UAV video feed was successfully transmitted and considered of sufficient quality to identify acute illness in runners participating in the 2022 Montreal Marathon (Canada).

Methods: Four UAVs equipped with high-resolution cameras were deployed at two pre-defined high-risk areas for medical incidents located within the last 500 meters of the race. The video footage was transmitted in real-time during four consecutive hours to a remote viewing station where four research assistants monitored it on large screens. Interruptions in live feed transmission and moments with inadequate field of view (FOV) on runners were documented.

Results: On September 25, 2022, a total of 6,916 athletes ran during the Montreal Marathon and Half Marathon. Out of the eight hours of video footage analyzed (four hours per high-risk area), 91.7% represented uninterrupted live video feed with an adequate view of the runners passing through the high-risk areas. There was a total of 18 live feed interruptions leading to a total interruption time of 22 minutes and 19 seconds (median interruption time of 32 seconds) and eight distinct moments with inadequate FOV on runners which accounted for 17 minutes and 33 seconds (median of 1 minute 47 seconds per moments with inadequate FOV). Active surveillance of drone-captured footage allowed early identification of two race participants in need of medical attention. Appropriate resources were dispatched, and UAV repositioning allowed for real-time viewing of the medical response.

Conclusion: Live video transmission from UAVs for medical surveillance of runners passing through higher risk segments of a marathon for four consecutive hours is feasible. Live feed interruptions and moments with inadequate FOV could be minimized through practice and additional equipment redundancy.

2022年蒙特利尔马拉松期间无人机实时视频馈送传输用于医疗监测的可行性。
简介:近年来,无人机越来越多地用于大规模聚集性事件中的医疗监视目的。没有研究调查无人机实时视频传输的可靠性,以准确识别需要医疗护理的痛苦比赛参与者。这项研究的目的是确定成功传输实时医疗监控无人机视频的时间比例,并认为其质量足以识别参加2022年蒙特利尔马拉松(加拿大)的参赛者的急性疾病。方法:四架配备高分辨率摄像头的无人机被部署在比赛最后500米内的两个预先定义的医疗事故高风险区域。视频片段在连续四个小时内实时传输到一个远程观看站,四名研究助理在大屏幕上进行监控。记录了直播传输中断和跑步者视野不足的瞬间。结果:2022年9月25日,共有6916名运动员参加了蒙特利尔马拉松和半程马拉松比赛。在分析的八个小时的视频片段中(每个高风险地区四个小时),91.7%的视频片段是不间断的实时视频,能够充分看到跑步者穿过高风险地区的画面。共有18次直播中断,导致总中断时间为22分19秒(中位中断时间为32秒),8次不同的FOV不足瞬间(占17分33秒)(FOV不足的瞬间中位1分47秒)。通过对无人机拍摄的视频进行积极监控,可以及早识别出两名需要医疗护理的参赛者。派遣了适当的资源,无人机重新定位可以实时查看医疗反应。结论:无人机实时视频传输用于连续四小时对通过马拉松高风险路段的跑步者进行医疗监控是可行的。通过实践和额外的设备冗余,可以最大限度地减少现场馈电中断和FOV不足的时刻。
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来源期刊
Prehospital and Disaster Medicine
Prehospital and Disaster Medicine Medicine-Emergency Medicine
CiteScore
3.10
自引率
13.60%
发文量
279
期刊介绍: Prehospital and Disaster Medicine (PDM) is an official publication of the World Association for Disaster and Emergency Medicine. Currently in its 25th volume, Prehospital and Disaster Medicine is one of the leading scientific journals focusing on prehospital and disaster health. It is the only peer-reviewed international journal in its field, published bi-monthly, providing a readable, usable worldwide source of research and analysis. PDM is currently distributed in more than 55 countries. Its readership includes physicians, professors, EMTs and paramedics, nurses, emergency managers, disaster planners, hospital administrators, sociologists, and psychologists.
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