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Open AccessArticle

Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application

Department of Geomatics Engineering, University of Calgary, 2500, University Drive, NW, Calgary, AB T2N 1N4, Canada
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Academic Editor: Stefano Mariani
Micromachines 2015, 6(8), 1100-1134; https://doi.org/10.3390/mi6081100
Received: 1 June 2015 / Revised: 23 July 2015 / Accepted: 28 July 2015 / Published: 14 August 2015
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
Low-cost inertial and motion sensors embedded on smartphones have provided a new platform for dynamic activity pattern inference. In this research, a comparison has been conducted on different sensor data, feature spaces and feature selection methods to increase the efficiency and reduce the computation cost of activity recognition on the smartphones. We evaluated a variety of feature spaces and a number of classification algorithms from the area of Machine Learning, including Naive Bayes, Decision Trees, Artificial Neural Networks and Support Vector Machine classifiers. A smartphone app that performs activity recognition is being developed to collect data and send them to a server for activity recognition. Using extensive experiments, the performance of various feature spaces has been evaluated. The results showed that the Bayesian Network classifier yields recognition accuracy of 96.21% using four features while requiring fewer computations. View Full-Text
Keywords: MEMS sensor; motion recognition; mobile computing; smart phones; gyroscope; accelerometers; pattern classification MEMS sensor; motion recognition; mobile computing; smart phones; gyroscope; accelerometers; pattern classification
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Saeedi, S.; El-Sheimy, N. Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application. Micromachines 2015, 6, 1100-1134.

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