A research on CSI-based human motion detection in complex scenarios

A method for detecting human motion in complex scenarios based on Channel State Information (CSI) is presented. First, the sensitivity of CSI phase information to human motion is explored, especially to the strenuous motion. Through a large number of experiments, the influence of human motion on CSI phase is found out, and the characteristics of signal changes are extracted. The One-class Support Vector Machine (OSVM) in machine learning is used to detect the multi-target strenuous human motion. Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) conditions are studied in the case of obstacles appearing in the wireless link when human motion occurred. LOS and NLOS are identified by the skewness of the channel impulse response (CIR) distribution. After identifying the LOS condition and NLOS condition in the current environment, the human motion is analyzed and detected, which further improves the accuracy of human motion detection from 70% to 91%.

[1]  David Wetherall,et al.  Tool release: gathering 802.11n traces with channel state information , 2011, CCRV.

[2]  Daqing Zhang,et al.  RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices , 2017, IEEE Transactions on Mobile Computing.

[3]  Hui Xiong,et al.  Performing Joint Learning for Passive Intrusion Detection in Pervasive Wireless Environments , 2010, 2010 Proceedings IEEE INFOCOM.

[4]  Kaishun Wu,et al.  We Can Hear You with Wi-Fi! , 2016, IEEE Trans. Mob. Comput..

[5]  Yunhao Liu,et al.  Non-Invasive Detection of Moving and Stationary Human With WiFi , 2015, IEEE Journal on Selected Areas in Communications.

[6]  Yunhao Liu,et al.  LiFi: Line-Of-Sight identification with WiFi , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[7]  Hui Xiong,et al.  An Adaptive Framework Coping with Dynamic Target Speed for Device-Free Passive Localization , 2015, IEEE Transactions on Mobile Computing.

[8]  Wei Wang,et al.  Understanding and Modeling of WiFi Signal Based Human Activity Recognition , 2015, MobiCom.

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

[10]  Shaojie Tang,et al.  Electronic frog eye: Counting crowd using WiFi , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[11]  Siyu Jiang,et al.  Whole-home gesture recognition using wireless signals (demo) , 2013, SIGCOMM.

[12]  Lu Wang,et al.  Pilot: Passive Device-Free Indoor Localization Using Channel State Information , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems.

[13]  Kaishun Wu,et al.  WiFall: Device-free fall detection by wireless networks , 2017, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[14]  Wei Wang,et al.  Keystroke Recognition Using WiFi Signals , 2015, MobiCom.

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

[16]  Shyamnath Gollakota,et al.  Bringing Gesture Recognition to All Devices , 2014, NSDI.

[17]  Moustafa Youssef,et al.  CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments , 2007, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[18]  Xu Chen,et al.  Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi , 2015, MobiHoc.

[19]  Yunhao Liu,et al.  PADS: Passive detection of moving targets with dynamic speed using PHY layer information , 2014, 2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS).