Research Article
Fine-Grained Transportation Mode Recognition Using Mobile Phones and Foot Force Sensors
@INPROCEEDINGS{10.1007/978-3-642-40238-8_9, author={Zelun Zhang and Stefan Poslad}, title={Fine-Grained Transportation Mode Recognition Using Mobile Phones and Foot Force Sensors}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 9th International Conference, MobiQuitous 2012, Beijing, China, December 12-14, 2012. Revised Selected Papers}, proceedings_a={MOBIQUITOUS}, year={2013}, month={9}, keywords={Transportation Mode Recognition Foot force sensor GPS Accelerometer}, doi={10.1007/978-3-642-40238-8_9} }- Zelun Zhang
Stefan Poslad
Year: 2013
Fine-Grained Transportation Mode Recognition Using Mobile Phones and Foot Force Sensors
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-642-40238-8_9
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.