2016/09/27 by Peter J. Polack, Shang-Tse Chen, Polack, Peter J +11 · 1 citation
Computer Science · #Advanced Text Analysis Techniques #Data Visualization and Analytics #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.1609.08535
openalex publication_date 2016/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The advent of mobile health technologies presents new challenges that existing visualizations, interactive tools, and algorithms are not yet designed to support. In dealing with uncertainty in sensor data and high-dimensional physiological records, we must seek to improve current tools that make sense of health data from traditional perspectives in event-based trend discovery. With Chronodes, a system developed to help researchers collect, interpret, and model mobile health (mHealth) data, we posit a series of interaction techniques that enable new approaches to understanding and exploring event-based data. From numerous and discontinuous mobile health data streams, Chronodes finds and visualizes frequent event sequences that reveal common chronological patterns across participants and days. By then promoting the sequences as interactive elements, Chronodes presents opportunities for finding, defining, and comparing cohorts of participants that exhibit particular behaviors. We applied Chronodes to a real 40GB mHealth dataset capturing about 400 hours of data. Through our pilot study with 20 behavioral and biomedical health experts, we gained insights into Chronodes' efficacy, limitations, and potential applicability to a wide range of healthcare scenarios.