Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients

D. Adu-Gyamfi, Fengli Zhang
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Abstract

Asymptomatic patients (AP) travel through neighborhoods in communities. The mobility dynamics of the AP makes it hard to tag them with specific interests. The lack of efficient monitoring systems can enable the AP to infect several vulnerable people in the communities. This article studied the monitoring of AP through their mobility and trajectory towards reducing the stress of socio-economic complications in the case of pandemics. Mobility and Trajectory based Technique for Monitoring Asymptomatic Patients (MTT-MAP) was established. The time-ordered spatial and temporal trajectory records of the AP were captured through their activities. A grid-based index data structure was designed based on network topology, graph theory and trajectory analysis to cater for the continuous monitoring of the AP over time. Also, concurrent object localisation and recognition, branch and bound, and multi-object instance strategies were adopted. The MTT-MAP has shown efficient when experimented with GeoLife dataset and can be integrated with state-of-the-art patients monitoring systems.
流动性和基于轨迹的技术监测无症状患者
无症状患者(AP)在社区中穿行。美联社的流动性动态使得很难给他们贴上特定兴趣的标签。缺乏有效的监测系统可以使AP感染社区中的一些弱势群体。本文研究了在流行病的情况下,通过AP的流动性和减少社会经济复杂性压力的轨迹来监测AP。建立了基于移动和轨迹的无症状患者监测技术(MTT-MAP)。通过它们的活动捕捉到AP的时空轨迹记录。基于网络拓扑、图论和轨迹分析,设计了基于网格的索引数据结构,以满足对AP的持续监测。采用并行目标定位与识别、分支与定界、多目标实例策略。在与GeoLife数据集的实验中,MTT-MAP显示出了效率,并且可以与最先进的患者监测系统集成。
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