An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data

An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data

Abstract Background Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide...

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Bibliographic Details
Main Authors: Duncan S. Procter, Angie S. Page, Ashley R. Cooper, Claire M. Nightingale, Bina Ram, Alicja R. Rudnicka, Peter H. Whincup, Christelle Clary, Daniel Lewis, Steven Cummins, Anne Ellaway, Billie Giles-Corti, Derek G. Cook, Christopher G. Owen
Format: Article
Language:English
Published: BMC 2018-09-01
Series:International Journal of Behavioral Nutrition and Physical Activity
Subjects:
Machine learning
Xgboost
Active travel
Travel mode
Physical activity
GPS
Online Access:http://link.springer.com/article/10.1186/s12966-018-0724-y

Internet

http://link.springer.com/article/10.1186/s12966-018-0724-y

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