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2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2016)
Sydney, Australia
March 14, 2016 to March 19, 2016
ISBN: 978-1-4673-8778-1
pp: 1-9
He Du , Northwestern Polytechnical University, Xi'an, China
Zhiwen Yu , Northwestern Polytechnical University, Xi'an, China
Fei Yi , Northwestern Polytechnical University, Xi'an, China
Zhu Wang , Northwestern Polytechnical University, Xi'an, China
Qi Han , Colorado School of Mines, Colorado, USA
Bin Guo , Northwestern Polytechnical University, Xi'an, China
ABSTRACT
Monitoring group mobility and structure is crucial for public safety management and emergency evacuation. In this paper, we propose a fine-grained mobility classification and structure recognition approach for social groups based on hybrid sensing using mobile devices. First, we present a method which classifies group mobility into four levels, including stationary, strolling, walking and running. Second, by combining mobile sensing and Wi-Fi signals, a novel relative position relationship estimation algorithm is developed to understand moving group structures of different shapes. We have conducted real-life experiments in which eight volunteers form two to three small groups moving in a teaching building with different speed and structures. Experimental results show that our approach achieves an accuracy of 99.5% in mobility classification and about 80% in group structure recognition.
INDEX TERMS
Legged locomotion, Acceleration, Feature extraction, Monitoring, IEEE 802.11 Standard, Accelerometers, Mobile handsets
CITATION

H. Du, Z. Yu, F. Yi, Z. Wang, Q. Han and B. Guo, "Group mobility classification and structure recognition using mobile devices," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Sydney, Australia, 2016, pp. 1-9.
doi:10.1109/PERCOM.2016.7456523
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