Computer Science ›› 2014, Vol. 41 ›› Issue (10): 27-30.doi: 10.11896/j.issn.1002-137X.2014.10.006

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LiveData—A Data Collecting System Based on Sensors in Smart Phones

WANG Zhong-wei and SUN Guang-zhong   

  • Online:2018-11-14 Published:2018-11-14

Abstract: With the emergence of every build-in sensor in the smart phone,users can collect,analyze and mine more useful information.We introduced LiveData,an application based on Android platform which is used to collect sensor data.Using 280 thousand data records collected by LiveData,we distinguished the behavior of users by extracting some attributes.We also analyzed the impact of different sensors and different data collection environments on experiment results.

Key words: Smart phone,Sensor,Activity recognition

[1] Lu H,Yang J,Liu Z,et al.The Jigsaw continuous sensing engine for mobilephone applications[C]∥Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems.ACM New York,2010:71-84
[2] Hattori,Technol Y I O,Kitakyushu,et al.A Large Scale Gathe-ring System for Activity Data with Mobile[C]∥Proceedings of the 15th Annual International Symposium on Wearable Compu-ters.2011:97-100
[3] Rachuri K,Musolesi M,Mascolo C.Energy-Accuracy Trade-offs in Querying Sensor Data for Continuous Sensing Mobile Systems[C]∥Proc.of Mobile Context-Awareness Workshop.2010
[4] Zheng Yu,Liu Li-ke,Wang Long-hao,et al.Learning transpor-tation mode from raw gps data for geographic applications on the Web[C]∥Proceedings of the 17th international conference on World Wide Web.ACM New York,2008:247-256
[5] Li Quan-nan,Yu Zheng-chen,Xie Xing.Mining user similarity based on location history[C]∥Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in Geographic Information Systems.ACM New York,2008
[6] Miluzzo E,Lane N,Fodor K,et al.Campbell.Sensing meets mo-bile social networks:The design,implementation and evaluation of the CenceMe application[C]∥Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems.ACM New York,2008:337-350
[7] Ouchi K,Doi M.Living activity recognition using off-the-shelf sensors on mobile phones[J].annals of telecommunications,2012,67(7-8):387-395
[8] Bao L,Intille S S.Activity recognition from user annotated acceleration data[C]∥Proceedings of the 2nd International Conference on Pervasive Computing.2004:1-17
[9] Lane N D,Xu Y,Lu H,et al.Enabling large-scale human activity inference on smartphones using community similarity networks (csn)[C]∥Proceedings of the 13th International Confe-rence on Ubiquitous Computing.ACM New York,2011:355-364
[10] Liao L,Patterson D J,Fox D,et al.Learning and InferringTransportation Routines[C]∥Proceedings of the National Conference on Artificial Intelligence.2007:311-331
[11] Patterson D J,Liao L,Fox A D.Inferring High-Level Behavior from Low-Level Sensors[C]∥Proceedings of the 13th International Conference on Ubiquitous Computing.2003:73-89
[12] Tukey J W,Cooley J W.An Algorithm for the Machine Calculation of Complex Fourier Series[J].Mathematics of Computation,1965,19(90):297-301

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