Identifying active travel behaviors in challenging environments using GPS, accelerometers and machine learning algorithms

Identifying active travel behaviors in challenging environments using GPS, accelerometers and machine learning algorithms

Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper we present a supervised machine learning method for transporta...

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Bibliographic Details
Main Authors: Katherine eEllis, Suneeta eGodbole, Simon eMarshall, Gert eLanckriet, John eStaudenmayer, Jacqueline eKerr
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-04-01
Series:Frontiers in Public Health
Subjects:
physical activity
GPS
accelerometer
activity recognition
random forest
active travel
Online Access:http://journal.frontiersin.org/Journal/10.3389/fpubh.2014.00036/full

Internet

http://journal.frontiersin.org/Journal/10.3389/fpubh.2014.00036/full

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