Evaluation of Clustering Techniques for GPS Phenotyping Using Mobile Sensor Data

Published in PEARC 2020 - Trending now – machine learning and artificial intelligence, 2020

Recommended citation: Tschirhart, Zachary S., and Karl W. Schulz. "Evaluation of Clustering Techniques for GPS Phenotyping Using Mobile Sensor Data." Practice and Experience in Advanced Research Computing. 2020. 364-371. https://doi.org/10.1145/3311790.3396665

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.

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