Paper Info
Title
Automatic physical activity and in-vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques.
Abstract
Due to the advancement of tracking technology, a large quantity of movement data has been collected and analyzed in various research domains. In human mobility and physical activity (PA) research, GPS trajectories and the capabilities of geographic information systems (GIS) facilitate a better understanding of the associations between PA and various environmental factors taking individuals' daily travels into account. PA research, however, needs to widen its focus from the intensity of PA to types of PA, which may provide useful clues for understanding specific health behaviors in particular geographic contexts. This study proposes and develops an algorithm to automatically classify PA types and in-vehicle status using GPS and accelerometer data. Walking, standing, jogging, biking and sedentary/in-vehicle statuses are identified through hierarchical classification processes based on machine learning and geospatial techniques. The proposed algorithm achieved high predictive accuracy on real-world GPS and accelerometer data. It can greatly reduce participants' and researchers' burdens by automatically identifying PA types and in-vehicle status for human mobility research, which is also known as travel mode imputation in transportation research.
Year
DOI
Venue
2018
10.1111/tgis.12485
TRANSACTIONS IN GIS
Field
DocType
Volume
Data mining,Computer science,Accelerometer,Global Positioning System,Artificial intelligence,Machine learning
Journal
22.0
Issue
ISSN
Citations 
6.0
1361-1682
0
PageRank 
References 
Authors
0.34
21
2
Name
Order
Citations
PageRank
Kangjae Lee100.34
Mei-Po Kwan233645.13