Sensors | |
Beyond Where to How: A Machine Learning Approach for Sensing Mobility Contexts Using Smartphone Sensors |
|
Robert E. Guinness1  | |
[1] Finnish Geospatial Research Institute, Geodeetinrinne 2, FI-02430 Masala, Finland; E-Mail | |
关键词: context awareness; smartphone sensors; machine learning; classification; mobility context; supervised learning; | |
DOI : 10.3390/s150509962 | |
来源: mdpi | |
【 摘 要 】
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 (<5 s). We investigated a wide range of supervised learning techniques for classification, including decision trees (DT), support vector machines (SVM), naive Bayes classifiers (NB), Bayesian networks (BN), logistic regression (LR), artificial neural networks (ANN) and several instance-based classifiers (KStar, LWLand IBk). Applying ten-fold cross-validation, the best performers in terms of correct classification rate (
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202003190013158ZK.pdf | 910KB | download |