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. 2017 Sep:2017:237-240.
doi: 10.1145/3123024.3123170.

mHealth Visual Discovery Dashboard

Dezhi Fang  1 , Fred Hohman  1 , Peter Polack  1 , Hillol Sarker  2 , Minsuk Kahng  3 , Moushumi Sharmin  4 , Mustafa al'Absi  5 , Duen Horng Chau  1
Affiliations

mHealth Visual Discovery Dashboard

Dezhi Fang et al. Proc ACM Int Conf Ubiquitous Comput. 2017 Sep.

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.

Keywords: H.5.m [Information interfaces and presentation (e.g., HCI)]: Miscellaneous; J.3 [Computer Applications]: Life and Medical Sciences; Visual analytics; health informatics; motif discovery; time series data.

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Figures

Figure 1
Figure 1
The Discovery Dashboard interface showing data from a mobile sensor study. Each row corresponds to one participant's data. A user-defined motif (for participant 6012) is selected, and the system automatically finds similar motifs across all participants and highlights them in yellow. This particular motif is a recurring pattern for participant 6012, often found near smoking lapses (vertical red dotted lines).
Figure 2
Figure 2
The Discovery Dashboard contains a number of options that are accessible from the “Analyze Particpants” button. Researchers can (1) align the data chronologically or by the first smoking lapse, (2) filter participants by name, number of lapses, and the day of their first lapse, and (3) search for time series motifs.
See this image and copyright information in PMC

References

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