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.
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 language | English |
|---|---|
| Title of host publication | UbiComp '18 |
| Subtitle of host publication | Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers |
| Place of Publication | New YorkNY |
| Publisher | Association for Computing Machinery |
| Pages | 283-286 |
| ISBN (Print) | 9781450359665 |
| DOIs | |
| Publication status | Published - Oct 2018 |
| Event | 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers - Singapore, Singapore Duration: 8 Oct 2018 → 12 Oct 2018 |
Conference
| Conference | 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers |
|---|---|
| Abbreviated title | UbiComp '18 |
| Country/Territory | Singapore |
| City | Singapore |
| Period | 8/10/18 → 12/10/18 |
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