Abstract
Transportation or travel mode recognition plays an important role in enabling us to derive transportation profiles, e.g., to assess how eco-friendly our travel is, and to adapt travel information services such as maps to the travel mode. However, current methods have two key limitations: low transportation mode recognition accuracy and coarse-grained transportation mode recognition capability. In this paper, we propose a new method which leverages a set of wearable foot force sensors in combination with the use of a mobile phone’s GPS (FF+GPS) to address these limitations. The transportation modes recognised include walking, cycling, bus passenger, car passenger, and car driver. The novelty of our approach is that it provides a more fine-grained transportation mode recognition capability in terms of reliably differentiating bus passenger, car passenger and car driver for the first time. Result shows that compared to a typical accelerometer-based method with an average accuracy of 70%, the FF+GPS based method achieves a substantial improvement with an average accuracy of 95% when evaluated using ten individuals.
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© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Zhang, Z., Poslad, S. (2013). Fine-Grained Transportation Mode Recognition Using Mobile Phones and Foot Force Sensors. In: Zheng, K., Li, M., Jiang, H. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40238-8_9
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DOI: https://doi.org/10.1007/978-3-642-40238-8_9
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