Beyond where to how: a machine learning approach for sensing mobility contexts using smartphone sensors

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2015-04, Vol.15 (5), p.9962-9985
Main Author: Guinness, Robert E
Format: Article
Language:English
Subjects:
Accelerometers
Algorithms
Bayesian analysis
Cellular telephones
Classification
Classifiers
context awareness
Learning theory
Machine learning
mobility context
Neural networks
Sensors
smartphone sensors
Smartphones
supervised learning
Support vector machines
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Summary:This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone user's mobility activities, including walking, running, driving and using a bus or train, in real-time or near-real-time (
ISSN:1424-8220
1424-8220
DOI:10.3390/s150509962