Mobility Profiler

This profiler is able to estimate a user’s mobility profile based on anonymized and lightweight smartphone data. In particular, this system is composed of (1) a web analytics platform, able to analyze multimodal sensing traces and improve our understanding of complex mobility patterns, and (2) a smartphone application, able to show a user’s profile generated locally in the form of a spider graph. In particular, this system uses anonymized and privacy-friendly data and methods, obtained thanks to the combination of Wi-Fi traces, activity detection and graph theory, made available independent of any personal information.

Contact: Sébastien Faye
Project: MAMBA



Coming soon!


More information

Main publications:

  • S. Faye, I. Tahirou, and T. Engel, “Demo Abstract: Human Mobility Profiling Using Privacy-Friendly Wi-Fi and Activity Traces,” in The 14th ACM Conference on Embedded Networked Sensor Systems (SenSys 2016), Stanford, CA, USA, 2016.
  • S. Faye and T. Engel, “Understanding User Daily Mobility Using Mobile and Wearable Sensing Systems,” in International Conference on Information and Communications Technology Convergence 2016 (ICTC 2016), Jeju Island, Korea, 2016.