Department of Emergency and Disaster Medicine, Hamamatsu University School of Medicine, Hamamatsu, 431-3125, Japan.
Center for Clinical Research, Hamamatsu University Hospital, Hamamatsu, 431-3125, Japan.
Sci Rep. 2021 Mar 3;11(1):5042. doi: 10.1038/s41598-021-84575-1.
In local and global disaster scenes, rapid recognition of victims' breathing is vital. It is unclear whether the footage transmitted from small drones can enable medical providers to detect breathing. This study investigated the ability of small drones to evaluate breathing correctly after landing on victims' bodies and hovering over them. We enrolled 46 medical workers in this prospective, randomized, crossover study. The participants were provided with envelopes, from which they were asked to pull four notes sequentially and follow the written instructions ("breathing" and "no breathing"). After they lied on the ground in the supine position, a drone was landed on their abdomen, subsequently hovering over them. Two evaluators were asked to determine whether the participant had followed the "breathing" or "no breathing" instruction based on the real-time footage transmitted from the drone camera. The same experiment was performed while the participant was in the prone position. If both evaluators were able to determine the participant's breathing status correctly, the results were tagged as "correct." All experiments were successfully performed. Breathing was correctly determined in all 46 participants (100%) when the drone was landed on the abdomen and in 19 participants when the drone hovered over them while they were in the supine position (p < 0.01). In the prone position, breathing was correctly determined in 44 participants when the drone was landed on the abdomen and in 10 participants when it was kept hovering over them (p < 0.01). Notably, breathing status was misinterpreted as "no breathing" in 8 out of 27 (29.6%) participants lying in the supine position and 13 out of 36 (36.1%) participants lying in the prone position when the drone was kept hovering over them. The landing points seemed wider laterally when the participants were in the supine position than when they were in the prone position. Breathing status was more reliably determined when a small drone was landed on an individual's body than when it hovered over them.
在本地和全球灾难场景中,快速识别受害者的呼吸至关重要。目前尚不清楚小型无人机传输的视频画面是否能让医疗人员检测到呼吸。本研究旨在调查小型无人机降落在受害者身上并在其上方盘旋后正确评估呼吸的能力。我们招募了 46 名医务人员参与这项前瞻性、随机、交叉研究。参与者被提供信封,他们需要从中依次抽取四张纸条并按照纸条上的说明(“有呼吸”和“无呼吸”)进行操作。参与者躺在地上呈仰卧位后,将无人机降落在他们的腹部,随后在其上方盘旋。两名评估者根据从无人机摄像头传输的实时视频判断参与者是否遵循了“有呼吸”或“无呼吸”的指令。当参与者处于俯卧位时,进行了相同的实验。如果两名评估者都能正确判断参与者的呼吸状态,则结果标记为“正确”。所有实验均成功完成。当无人机降落在腹部时,所有 46 名参与者(100%)的呼吸均被正确判断,而当无人机在其上方盘旋时,19 名参与者(p<0.01)的呼吸被正确判断。当无人机降落在腹部时,44 名参与者(100%)的呼吸在俯卧位时被正确判断,而当无人机在其上方盘旋时,10 名参与者(27.3%)的呼吸被正确判断(p<0.01)。值得注意的是,当无人机保持盘旋状态时,27 名仰卧位参与者中有 8 名(29.6%)和 36 名俯卧位参与者中有 13 名(36.1%)被错误地判断为“无呼吸”。当参与者处于仰卧位时,无人机的着陆点看起来比处于俯卧位时更宽。当小型无人机降落在个体身上时,呼吸状态的判断更可靠。