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Sensors 2015, 15(5), 9962-9985; doi:10.3390/s150509962

Beyond Where to How: A Machine Learning Approach for Sensing Mobility Contexts Using Smartphone Sensors

Finnish Geospatial Research Institute, Geodeetinrinne 2, FI-02430 Masala, Finland
This paper is an extended version of a paper paper entitled “Beyond Where to How: A Machine Learning Approach for Sensing Mobility Contexts Using Smartphone Sensors” presented at 26th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS + 2013), Nashville, Tennessee, 16–20 September 2013.
Academic Editor: Vittorio M.N. Passaro
Received: 26 February 2015 / Revised: 8 April 2015 / Accepted: 9 April 2015 / Published: 28 April 2015
(This article belongs to the Section Physical Sensors)
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Abstract

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 (i.e., recall) were DT (96.5%), BN (90.9%), LWL (95.5%) and KStar (95.6%). In particular, the DT-algorithm RandomForest exhibited the best overall performance. After a feature selection process for a subset of algorithms, the performance was improved slightly. Furthermore, after tuning the parameters of RandomForest, performance improved to above 97.5%. Lastly, we measured the computational complexity of the classifiers, in terms of central processing unit (CPU) time needed for classification, to provide a rough comparison between the algorithms in terms of battery usage requirements. As a result, the classifiers can be ranked from lowest to highest complexity (i.e., computational cost) as follows: SVM, ANN, LR, BN, DT, NB, IBk, LWL and KStar. The instance-based classifiers take considerably more computational time than the non-instance-based classifiers, whereas the slowest non-instance-based classifier (NB) required about five-times the amount of CPU time as the fastest classifier (SVM). The above results suggest that DT algorithms are excellent candidates for detecting mobility contexts in smartphones, both in terms of performance and computational complexity. View Full-Text
Keywords: context awareness; smartphone sensors; machine learning; classification; mobility context; supervised learning context awareness; smartphone sensors; machine learning; classification; mobility context; supervised learning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Guinness, R.E. Beyond Where to How: A Machine Learning Approach for Sensing Mobility Contexts Using Smartphone Sensors. Sensors 2015, 15, 9962-9985.

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