Bioengineering Research Group, Faculty of Engineering, The University of Nottingham, Nottingham, UK.
CenTre Neonatal Transport, University Hospitals of Leicester, Leicester, UK.
Proc Inst Mech Eng H. 2021 Apr;235(4):428-436. doi: 10.1177/0954411920985994. Epub 2021 Jan 9.
Transferring sick premature infants between hospitals increases the risk of severe brain injury, potentially linked to the excessive exposure to noise, vibration and driving-related accelerations. One method of reducing these levels may be to travel along smoother and quieter roads at an optimal speed, however this requires mass data on the effect of roads on the environment within ambulances. An app for the Android operating system has been developed for the purpose of recording vibration, noise levels, location and speed data during ambulance journeys. Smartphone accelerometers were calibrated using sinusoidal excitation and the microphones using calibrated pink noise. Four smartphones were provided to the local neonatal transport team and mounted on their neonatal transport systems to collect data. Repeatability of app recordings was assessed by comparing 37 journeys, made during the study period, along an 8.5 km single carriageway. The smartphones were found to have an accelerometer accurate to 5% up to 55 Hz and microphone accurate to 0.8 dB up to 80 dB. Use of the app was readily adopted by the neonatal transport team, recording more than 97,000 km of journeys in 1 year. To enable comparison between journeys, the 8.5 km route was split into 10 m segments. Interquartile ranges for vehicle speed, vertical acceleration and maximum noise level were consistent across all segments (within 0.99 m . s, 0.13 m · s and 1.4 dB, respectively). Vertical accelerations registered were representative of the road surface. Noise levels correlated with vehicle speed. Android smartphones are a viable method of accurate mass data collection for this application. We now propose to utilise this approach to reduce potential harmful exposure, from vibration and noise, by routing ambulances along the most comfortable roads.
在医院之间转移患病的早产儿会增加严重脑损伤的风险,这可能与过度暴露于噪声、振动和与行驶相关的加速度有关。减少这些水平的一种方法可能是沿着更平稳、更安静的道路以最佳速度行驶,然而这需要大量关于道路对救护车内部环境影响的数据。为此,已经开发了一种适用于 Android 操作系统的应用程序,用于记录救护车行驶过程中的振动、噪声水平、位置和速度数据。使用正弦波激励对智能手机加速度计进行校准,并用校准的粉红噪声对麦克风进行校准。向当地新生儿转运团队提供了四部智能手机,并将其安装在新生儿转运系统上以收集数据。通过比较研究期间进行的 37 次 8.5 公里单车道旅程,评估了应用程序记录的可重复性。发现智能手机的加速度计精度高达 5%,频率高达 55 Hz,麦克风精度高达 0.8 dB,频率高达 80 dB。新生儿转运团队很容易采用该应用程序,在 1 年内记录了超过 97000 公里的旅程。为了能够在旅程之间进行比较,将 8.5 公里的路线分成 10 米的段。在所有段内,车辆速度、垂直加速度和最大噪声水平的四分位范围都是一致的(分别在 0.99 m. s 、0.13 m · s 和 1.4 dB 内)。记录的垂直加速度代表了路面情况。噪声水平与车速相关。Android 智能手机是用于此应用的精确大数据采集的可行方法。我们现在提议利用这种方法,通过将救护车路由到最舒适的道路上来减少振动和噪声的潜在有害暴露。