Center for Applied Data Science (CADS), Winston-Salem State University, Winston-Salem, NC 27110, USA.
Department of Computer Science, Winston-Salem State University, Winston-Salem, NC 27110, USA.
Sensors (Basel). 2022 Oct 5;22(19):7546. doi: 10.3390/s22197546.
While there are numerous causes of waste in the healthcare system, some of this waste is associated with inefficiency. Among the proposed solutions to address inefficiency is clinic layout optimization. Such optimization depends on how operating resources and instruments are placed in the clinic, in what order they are accessed to attain a particular task, and the mobility of clinicians between different clinic rooms to accomplish different clinic tasks. Traditionally, such optimization research involves manual monitoring by human proctors, which is time consuming, erroneous, unproductive, and subjective. If mobility patterns in an indoor space can be determined automatically in real time, layout and operation-related optimization decisions based on these patterns can be implemented accurately and continuously in a timely fashion. This paper explores this application domain where precise localization is not required; however, the determination of mobility is essential on a real-time basis. Given that, this research explores how only mobile devices and their built-in Bluetooth received signal strength indicator (RSSI) can be used to determine such mobility. With a collection of stationary mobile devices, with their computational and networking capabilities and lack of energy requirements, the mobility of moving mobile devices was determined. The research methodology involves developing two new algorithms that use raw RSSI data to create visualizations of movements across different operational units identified by stationary nodes. Compared with similar approaches, this research showcases that the method presented in this paper is viable and can produce mobility patterns in indoor spaces that can be utilized further for data analysis and visualization.
虽然医疗系统存在许多浪费的原因，但其中一些浪费与效率低下有关。为了解决效率低下的问题，提出了一些解决方案，其中包括优化诊所布局。这种优化取决于如何在诊所中放置运营资源和仪器，以及它们以何种顺序被访问以完成特定任务，以及临床医生在不同诊所房间之间的移动以完成不同的诊所任务。传统上，这种优化研究涉及到由人类监督者进行的手动监测，这种方法既耗时、易错、低效，又主观。如果可以实时自动确定室内空间中的移动模式，则可以根据这些模式实时准确地实施布局和操作相关的优化决策。本文探讨了这个应用领域，在这个领域中不需要精确的定位；然而，实时确定移动性是必不可少的。考虑到这一点，本研究探讨了仅使用移动设备及其内置蓝牙接收信号强度指示器（RSSI）如何确定这种移动性。通过使用一组固定的移动设备，利用其计算和网络功能以及缺乏能源需求，可以确定移动移动设备的移动性。研究方法涉及开发两种新算法，这些算法使用原始 RSSI 数据创建可视化的运动，跨越由固定节点识别的不同操作单元。与类似的方法相比，本研究表明，本文提出的方法是可行的，可以在室内空间中生成可进一步用于数据分析和可视化的移动模式。
