Efficient vehicle-to-pedestrian exchange of medical data: an empirical model with preliminary results

G. Marfia, M. Roccetti, C. Palazzi, A. Amoroso
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引用次数: 12

Abstract

Ambulances and emergency vehicles (buses and taxis as well), if equipped with wireless devices, can be exploited to harvest medical data during unexpected events and also on a daily basis, from all those patients that require a constant monitoring of health conditions. Ambulances can be utilized as trusted intermediaries to transport medical information, at little cost, to hospital central servers. Patients equipped with physiological sensors connected to wireless devices could dump, during each contact, all the medical information collected so far, thus utilizing emergency vehicles as data mules. Inevitably, contact times may be short and not sufficient to transfer all of the information collected from a patient's medical sensors. In such cases, computing in advance, or during the very initial phase of a data transfer, an estimate of how long a contact time will last is key to maximize the utility of any successfully transmitted chunks, in general of different sizes and priorities, of medical data. In this paper we address the problem of predicting patient-vehicle contact times, through an empirical model based on real-world experiments focused on the key sections of a road, which most influence the average speed of an emergency vehicle that traverses it. Our preliminary results are encouraging, as they indicate that it is possible to predict the time an emergency vehicle will spend traversing a given road segment within one third of its traversal.
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高效的车辆到行人的医疗数据交换:一个具有初步结果的经验模型
救护车和急救车(公共汽车和出租车也是如此)如果配备了无线设备,就可以在突发事件期间和每天从所有需要持续监测健康状况的患者那里收集医疗数据。救护车可以作为可信的中介,以低廉的成本将医疗信息传送到医院的中央服务器。配备了连接到无线设备的生理传感器的患者可以在每次接触期间转储迄今为止收集的所有医疗信息,从而利用急救车辆作为数据骡子。不可避免的是,接触时间可能很短,不足以传输从患者医疗传感器收集的所有信息。在这种情况下,提前计算或在数据传输的初始阶段,估计接触时间将持续多长时间是最大限度地利用成功传输的医疗数据块(通常是不同大小和优先级的数据块)的关键。在本文中,我们通过一个基于真实世界实验的经验模型,解决了预测病人与车辆接触时间的问题,该模型集中在道路的关键路段,这些路段对穿越该路段的急救车辆的平均速度影响最大。我们的初步结果令人鼓舞,因为它们表明,有可能在三分之一的时间内预测紧急车辆穿越给定路段所花费的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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