What are Good Situations for Running?: A Machine Learning Study using Mobile and Geographical Data
| Autor: | Wang, S., Scheider, S., Sporrel, K., Deutekom, Marije, Timmer, Joris, Kröse, Ben J.A., Sub Intelligent Systems, Urban Accessibility and Social Inclusion |
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| Přispěvatelé: | Sub Intelligent Systems, Urban Accessibility and Social Inclusion |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Hierarchical agglomerative clustering
020205 medical informatics Relation (database) Computer science Big data Physical activity physical activity 02 engineering and technology Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine environmental situations big data 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Extensive data running Humans 030212 general & internal medicine Situational ethics Empirical evidence mobile data mining Original Research business.industry lcsh:Public aspects of medicine Environmental and Occupational Health Public Health Environmental and Occupational Health lcsh:RA1-1270 Mobile Applications machine learning Artificial intelligence Public Health business computer |
| Zdroj: | Frontiers in Public Health, 8, 1. Frontiers Media S.A. Frontiers in Public Health, Vol 8 (2021) Frontiers in Public Health |
| ISSN: | 2296-2565 |
| Popis: | Running is a popular form of physical activity. Personal, social, and environmental determinants influence the engagement of the individual. To get insight in the relation between running behavior and external situations for different types of users, we carried out an extensive data mining study on large-scale datasets. We combined 4 years of historical running data (collected by a mobile exercise application from over 10K participants) with weather, topographical and demographical datasets. We introduce weighted frequent item mining for the analysis of the data. In this way, we capture temporal and environmental situations that frequently associate with different running performances. The results show that specific temporal and environmental situations (hour in a day, day in a week, temperature, distance to residential areas, and population density) influence the running performance of users more than other situational features. Hierarchical agglomerative clustering on the running data is used to split runners in two clusters (with sustained and less sustained running behavior). We compared the two groups of runners and found that runners with less sustained behavior are more sensitive to the environmental situations (especially several weather and location related features, such as temperature, weather type, distance to the nearest park) than regular runners. Further analysis focused on the situational features for the less sustained runners. Results show that specific feature values correspond to a better or worse running distance. Not only the influence of individual features was examined but also the interplay between features. Our findings provide important empirical evidence that the role of external situations in the running behavior of individuals can be derived from analysis of the combined historical datasets. This opens up a large potential to take those situations specifically into consideration when supporting individuals which show less sustained behavior. |
| Databáze: | OpenAIRE |
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