A data-driven study on preferred situations for running

Shihan Wang, Joris Alexander Timmers, Simon Scheider, Karlijn Sporrel, Zeynep Akata, Ben Kröse

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

We analyzed a large data set from a mobile exercise application to find the preferred running situations of a large number of users. We categorized the users according to
their running behaviors (i.e. regularly active, or rarely ac-tive over the year), then studied the influence of 15 features, including temporal, geographical and weather-based features for different user groups. We found that geographical features influence the behavior of less active runners.
Original languageEnglish
Title of host publicationUbiComp '18
Subtitle of host publicationProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
Place of PublicationNew YorkNY
PublisherAssociation for Computing Machinery
Pages283-286
ISBN (Print)9781450359665
DOIs
Publication statusPublished - Oct 2018
Event2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers - Singapore, Singapore
Duration: 8 Oct 201812 Oct 2018

Conference

Conference2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
Abbreviated titleUbiComp '18
Country/TerritorySingapore
CitySingapore
Period8/10/1812/10/18

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