Evaluation of Clustering Techniques for GPS Phenotyping Using Mobile Sensor Data
Evaluation of Clustering Techniques for GPS Phenotyping Using Mobile Sensor Data
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DOI：
10.1145/3311790.3396665
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Zachary S. Tschirhart;K. Schulz
中科院分区：
文献类型：
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作者：
Zachary S. Tschirhart;K. Schulz
随着移动的智能手机的普及，健康研究人员越来越有兴趣利用这些常见的设备作为近实时数据的数据收集工具，以帮助远程监测，并支持分析和检测与各种健康相关的结果相关联的模式。因此，这项工作的重点是分析两个月来通过开源移动的平台收集的GPS数据，以支持正在进行的一项更大规模的研究，利用智能手机数据开发怀孕的数字表型。完成了对各种现成聚类方法的探索，以评估TACC Stampede2系统上292K非均匀样本的适度时间序列的准确性和运行时性能。受表型分析的启发，不仅需要评估GPS集群的物理坐标，而且还需要评估在高兴趣位置花费的累积时间，因此实施了两种额外的方法，以使用预处理步骤来促进集群时间累积，这对于提高聚类准确性和可扩展性也至关重要。
With the ubiquitousness of mobile smart phones, health researchers are increasingly interested in leveraging these commonplace devices as data collection instruments for near real-time data to aid in remote monitoring, and to support analysis and detection of patterns associated with a variety of health-related outcomes. As such, this work focuses on the analysis of GPS data collected through an open-source mobile platform over two months in support of a larger study being undertaken to develop a digital phenotype for pregnancy using smart phone data. An exploration of a variety of off-the-shelf clustering methods was completed to assess accuracy and runtime performance for a modest time-series of 292K non-uniform samples on the Stampede2 system at TACC. Motivated by phenotyping needs to not-only assess the physical coordinates of GPS clusters, but also the accumulated time spent at high-interest locations, two additional approaches were implemented to facilitate cluster time accumulation using a pre-processing step that was also crucial in improving clustering accuracy and scalability.