Mhealth visual Discovery Dashboard

Dezhi Fang, Minsuk Kahng, Fred Hohman, Moushumi Sharmin, Peter Polack, Mustafa Al'Absi, Hillol Sarker, Duen Horng Chau

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

1 Scopus citations

Abstract

We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. Discovery Dashboard emphasizes user freedom and flexibility during the data exploration process and enables researchers to do things previously challenging or impossible to do - in the web-browser and in real time. We demonstrate our system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking.

Original languageEnglish (US)
Title of host publicationUbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
PublisherAssociation for Computing Machinery, Inc
Pages237-240
Number of pages4
ISBN (Electronic)9781450351904
DOIs
StatePublished - Sep 11 2017
Event2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017 - Maui, United States
Duration: Sep 11 2017Sep 15 2017

Publication series

NameUbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers

Other

Other2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017
CountryUnited States
CityMaui
Period9/11/179/15/17

Bibliographical note

Funding Information:
We thank Soni Rraklli and Dayna Schleppenbach for co-ordination of the study and data collection, and Andrine Lemieux for data preparation. Research supported by grant U54EB020404, awarded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) through funds from the trans-NIH Big Data to Knowledge (BD2K) initiative, and by the NSF GRFP under Grant No. DGE-1148903.

Publisher Copyright:
Copyright © 2017 ACM.

Keywords

  • Health informatics
  • Motif discovery
  • Time series data
  • Visual analytics

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