BANTAY项目防潜逃人工智能隔离对象监测与室内定位

Derwin P. Ara, Aljohn Ric C. Marcaida, Arnold Ceigfred E. Seangoy, Angelino A. Pimentel
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引用次数: 0

摘要

隔离是限制和隔离接触过传染病的人的活动,以避免扩散的过程。被隔离的对象是人工监控的,病人往往会潜逃或“逃跑”。在菲律宾,缺乏与这一问题有关的研究,也没有商业上可用的类似技术。Bantay项目整合了人工智能物联网(AIoT)、室内定位系统和可穿戴技术,以缓解人员短缺,长期节省政府劳动力利用率,并预测我们当前和未来隔离设施中潜逃或“逃跑”的潜在感染个体。此外,它还包括用于监测被隔离对象心率和体温的信息系统开发,这将最终帮助政府应对类似的意外大流行。原型机将在被隔离对象身上放置后启动设备。该设备必须连接到路由器,然后必须校准传感器。网络界面应该能够接收并看到数据读数,包括温度、心率、被隔离对象的位置,以及通过红外读取设备的移除。温度、心率传感器和室内定位的测量值p值均大于0.05,接受原假设,证实实际产品和商业产品与Project Bantay的温度、心率和室内定位精度在统计学上是相同的。使用机器学习数据分析的假设数据集的反潜逃趋势表明,在四种固有多输出机器学习回归算法中,决策树回归算法在确定受试者从隔离设施潜逃的趋势方面可以输出更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quarantined-subject monitoring and indoor positioning with anti-absconding artificial intelligence (Project BANTAY)
Quarantine is the process of restricting and separating the movement of people who have been exposed to a contagious disease to avoid proliferation. Quarantined subjects were monitored manually, and patients tended to abscond or “run away.” In the Philippines, there was a lack of research and the absence of similar technology commercially available related to this matter. Project Bantay integrated Artificial Intelligence of Things (AIoT), indoor positioning systems, and wearable technology to alleviate the shortage of personnel, long-term savings on workforce utilization in the government, and predict absconding or “run-away” potentially infectious individuals in our current and future quarantine facilities. Also, it included information system development for monitoring quarantined subjects' heart rates and temperatures that would eventually help the government combat and prepare for a similar unexpected pandemic. The prototype would start by turning on the device after being placed on the quarantined subject. The device must be linked to the router, and the sensors must then be calibrated. The web interface should receive and be able to see data readings, including temperature, heart rate, the location of the subject being quarantined, and the removal of the device via an IR reading. The temperature, heart rate sensor, and indoor positioning all measured above a p-value of 0.05, which accepted the null hypothesis, confirming that the actual and commercial product versus Project Bantay's temperature, heart rate, and indoor positioning accuracy was statistically the same. The anti-absconding tendencies of the hypothetical dataset using machine learning data analytics showed that among four inherently multioutput machine learning regression algorithms, the Decision Tree Regression Algorithm could output a much better result in determining the tendencies of a subject to abscond from the quarantine facility.
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