Visual analysis of relationships between behavioral and physiological sensor data

Jennifer G. Kim, Melinda Snodgrass, Mary Pietrowicz, Karrie Karahalios

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

More and more people are collecting, organizing, and interpreting health data through off-the-shelf products such as Apple's Health Kit. People use these systems to collect steps taken, calories ingested, etc. These ecosystems also support the collection of physiological data. For example, users collect heart rate data during exercise, and even electro dermal activity data to help detect the onset of seizures. Analyzing physiological data, however, and relating it to specific behaviors or events, is challenging. In this paper, we present an 11-week, multi-session, participatory design case study, identify challenges in analyzing physiological and behavior data, and present BEDA, an analytics tool we developed to mitigate the challenges. The two primary data analysis challenges include: (1) interfacing multiple software programs required for capturing and analyzing the different data sources, and (2) extending the limited data analysis functionality within and across these software programs to support a wide range of physiological data analyses. BEDA resolves the fragmented analysis pipeline by integrating closely-related analysis tasks into a common interface. It also addresses the extensibility problem by integrating scripts that apply any custom or publicly-available function written in MATLAB or R. These scripts extend basic analytic capability, provide the analytic bridge between physiological and behavior data, and incorporate machine learning algorithms to highlight behaviors associated with physiological data. BEDA's capabilities mitigated the challenges of signal analysis and fragmented data sources, and motivated behavioral scientists to combine physiological measures with behavioral analysis. Although we developed this tool for a domain-specific case study, the use of the tool can be generalized to analyze any time-based data source or sources.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015
EditorsWai-Tat Fu, Prabhakaran Balakrishnan, Sanda Harabagiu, Fei Wang, Jaideep Srivatsava
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages170-179
Number of pages10
ISBN (Electronic)9781467395489
DOIs
StatePublished - Dec 8 2015
Event3rd IEEE International Conference on Healthcare Informatics, ICHI 2015 - Dallas, United States
Duration: Oct 21 2015Oct 23 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015

Other

Other3rd IEEE International Conference on Healthcare Informatics, ICHI 2015
CountryUnited States
CityDallas
Period10/21/1510/23/15

Keywords

  • Behavior analysis
  • Physiological sensor data analysis
  • Visual analytic tool

ASJC Scopus subject areas

  • Health Informatics

Fingerprint Dive into the research topics of 'Visual analysis of relationships between behavioral and physiological sensor data'. Together they form a unique fingerprint.

Cite this