Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results

Computers, Environment and Urban Systems

Volumn 67, Issue , 2018, Pages 124-131

  Lee, Kangjae ^a   Kwan, Mei Po ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Accelerometer data; GIS; GPS trajectory; Machine learning; Physical activity classification

Indexed keywords

ACCELEROMETERS; DECISION TREES; GEOGRAPHIC INFORMATION SYSTEMS; LEARNING SYSTEMS; SMARTPHONES;

ACCELEROMETER DATA; FREE LIVINGS; GPS TRAJECTORY; GRADIENT BOOSTING; LIVING CONDITIONS; MACHINE-LEARNING; PHYSICAL ACTIVITY; PHYSICAL ACTIVITY CLASSIFICATIONS; RANDOM FORESTS; SMART PHONES;

TRAJECTORIES;

ACCELEROMETER; CLASSIFICATION; GIS; GPS; MACHINE LEARNING; MOBILE PHONE; PHYSICAL ACTIVITY; PREDICTION; TRAJECTORY;

EID: 85033462552     PISSN: 01989715     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compenvurbsys.2017.09.012     Document Type: Article

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