An inference engine for smartphones to preprocess data and detect stationary and transportation modes |
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| Affiliation: | 1. Department of Computer Science, Amirkabir University of Technology, N. 424, Hafez Avenue, Tehran 15875-4413, Iran;2. Intelligent Transportation Systems Research Institute, Amirkabir University of Technology, Tehran, Iran;1. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China;2. College of Computer Science, Zhejiang University of Technology, Hangzhou, 310000, China;1. University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland;2. ETH Zurich, Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland |
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| Abstract: | A smartphone can be utilized as a cost-effective device for the purposes of intelligent transportation system. To detect the movement and the stationary statuses in the motorized and non-motorized modes, this study develops a new inference engine, including two sets of rules. The first sets of rules are defined by the related thresholds on the features of smartphone sensors while the second sets are extracted from the human knowledge to improve the results of the first rules. The experimental results reveal that by utilizing Inertial Measurement Unit (IMU) sensors in the proposed inference engine, it is possible to save 40% energy in comparison with the previous research. Moreover, this engine increases the accuracy of the motorized mode detection to 95.2% and determines the stationary states in motorized mode with 97.1% accuracy. |
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| Keywords: | Intelligent Transportation Systems Mode detection Smartphone sensors Inference engine |
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