Joint localization and activity recognition from ambient FM broadcast signals

Due to spatial diversity, RF signals derived from a FM broadcast station differ when they arrive at the receivers placed in various locations. Also, the FM signals will be altered by the change of ambient environment. Previous works focuse either the FM-based localization or activity recognition. In this study, we propose to simultaneously classify and localize activities conducted in proximity of an FM receiver. We conducted experiments and demonstrated that the location and activities of an individual can be distinguishable with a reasonable overall accuracy in a typical indoor environment from FM broadcast signals.

[1]  Yusheng Ji,et al.  RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals , 2014, IEEE Transactions on Mobile Computing.

[2]  Gregory D. Abowd,et al.  Farther Than You May Think: An Empirical Investigation of the Proximity of Users to Their Mobile Phones , 2006, UbiComp.

[3]  Stephan Sigg,et al.  Situation Awareness Based on Channel Measurements , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[4]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[5]  I. Anderson,et al.  Context Awareness via GSM Signal Strength Fluctuation ? , 2006 .

[6]  Katarzyna Wac,et al.  Getting closer: an empirical investigation of the proximity of user to their smart phones , 2011, UbiComp '11.

[7]  Yusheng Ji,et al.  Activity recognition from radio frequency data: Multi-stage recognition and features , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[8]  Eyal de Lara,et al.  Accurate GSM Indoor Localization , 2005, UbiComp.

[9]  Neal Patwari,et al.  Through-Wall Tracking Using Variance-Based Radio Tomography Networks , 2009, ArXiv.

[10]  Raffaele Bruno,et al.  Design and Analysis of a Bluetooth-Based Indoor Localization System , 2003, PWC.

[11]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[12]  Yusheng Ji,et al.  SenseWaves: Radiowaves for context recognition , 2011 .

[13]  Neal Patwari,et al.  Radio Tomographic Imaging with Wireless Networks , 2010, IEEE Transactions on Mobile Computing.

[14]  Yusheng Ji,et al.  Passive detection of situations from ambient FM-radio signals , 2012, UbiComp.

[15]  William G. Griswold,et al.  Mobility Detection Using Everyday GSM Traces , 2006, UbiComp.

[16]  Moustafa Youssef,et al.  RASID: A robust WLAN device-free passive motion detection system , 2011, 2012 IEEE International Conference on Pervasive Computing and Communications.

[17]  Nirvana Meratnia,et al.  Sensing Motion Using Spectral and Spatial Analysis of WLAN RSSI , 2007, EuroSSC.

[18]  Moustafa Youssef,et al.  Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments , 2009, IEEE Transactions on Mobile Computing.

[19]  Lionel M. Ni,et al.  Dynamic clustering for tracking multiple transceiver-free objects , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[20]  Neal Patwari,et al.  Spatial Models for Human Motion-Induced Signal Strength Variance on Static Links , 2011, IEEE Transactions on Information Forensics and Security.

[21]  Jesse Hoey,et al.  Sensor-Based Activity Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..