Passive, Device-Free Recognition on Your Mobile Phone: Tools, Features and a Case Study

We investigate the detection of activities and presence in the proximity of a mobile phone via the WiFi-RSSI at the phone. This is the first study to utilise RSSI in received packets at a mobile phone for the classification of activities. We discuss challenges that hinder the utilisation of WiFi PHY-layer information, recapitulate lessons learned and describe the hardware and software employed. Also, we discuss features for activity recognition (AR) based on RSSI and present two case studies. We make available our implemented tools for AR based on RSSI.

[1]  Sneha Kumar Kasera,et al.  Monitoring Breathing via Signal Strength in Wireless Networks , 2011, IEEE Transactions on Mobile Computing.

[2]  Youngwook Kim,et al.  Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[3]  I. Bratko,et al.  Information-based evaluation criterion for classifier's performance , 2004, Machine Learning.

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

[5]  Kent A. Spackman,et al.  Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning , 1989, ML.

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

[7]  M. Beigl,et al.  Challenges for device-free radio-based activity recognition , 2011 .

[8]  Yusheng Ji,et al.  RF-Based device-free recognition of simultaneously conducted activities , 2013, UbiComp.

[9]  Richard Howard,et al.  SCPL: Indoor device-free multi-subject counting and localization using radio signal strength , 2013, 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[10]  Tomoaki Ohtsuki,et al.  Ambient intelligence sensing using array sensor: device-free radio based approach , 2013, UbiComp.

[11]  Michael Beigl,et al.  Device-free and device-bound activity recognition using radio signal strength , 2013, AH.

[12]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[13]  Fadel Adib,et al.  See through walls with WiFi! , 2013, SIGCOMM.

[14]  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.