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Context-Free Fine-Grained Motion Sensing using WiFi. Changlai Du, Xiaoqun Yuan, Wenjing Lou, Thomas Hou Virginia Tech Wuhan University June. 13, 2018. Ubiquitous Wireless. Wireless communications are ubiquitous More wireless devices connect Internet 20 billion will be in use by 2020 [1]
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Context-Free Fine-Grained Motion Sensing using WiFi Changlai Du, Xiaoqun Yuan, Wenjing Lou, Thomas Hou Virginia Tech Wuhan University June. 13, 2018
Ubiquitous Wireless • Wireless communications are ubiquitous • More wireless devices connect Internet • 20 billion will be in use by 2020[1] • 99% connect to Internet[2] [1] Gartner, http://www.gartner.com/newsroom/id/3598917 [2] The Future of Wireless. http://circuitcellar.com/cc-blog/iot-the-future-of-wireless-connect-anywhere-solutions/
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WiTalk: Fine-Grained Motion Sensing • CSI-based Fine-grained motion sensing Scheme • One-time training, resilient to context change • Work accepted to SECON 18 • C. Du, X. Yuan, W. Lou, Y.T. Hou, Context-Free Fine-Grained Motion Sensing using WiFi. IEEE SECON 2018, accepted.
Literature Review • CSI based motion sensing has been used different fields • Human localization: ACM Computing Surveys13 • Activity detection: MobiCom15, MobiCom14 • Human authentication: Ubicomp16, IPSN16 • Health care: UbiComp16, MobiHoc15 • Fine-grained motion detection: MobiCom15, MobiHoc16, CCS16, MobiCom14
Research Positioning 2 1 3 4
CSI to Motion • Why we can detect human motion using CSI? • Short answer: Human motion changes the value of CSI • So inversely, we can infer human motion from CSI changes • CSI Path-Phase model • Human Motion cause a dynamic multipath component • In-phase: Constructive • Out-phase: Destructive
Problem Describe • A user is making a phone call • The user’s smartphone is connect to an AP • AP collects CSI streams • AP infers user’s speaking from CSI streams
Challenges • Fine-grained motion caused CSI variance is very tiny • Easily buried in noise and interferences • Efficient CSI stream denoising methods • CSI waveforms change with context • Training per context not acceptable • Find intrinsic features in CSI dynamics correlated to fine-grained motion only
CSI Denoising • Band Pass Filter • 3-order Butterworth filter • Remove high frequency noise • Remove human breathing interference(0.2-0.33Hz) • Keep mouth movement caused frequencies(2-5Hz)
CSI Denoising • PCA Based Filtering • CSI streams of different subcarriers correlate their variations • Chose the second principal component as the filter result
Feature Extraction • Use the spectrogram for different syllables to extract the features of CSI streams • CSI-Speed model • Extract 3 contour lines of the spectrograms • Contour lines reduced computation cost • DTW to deal with different speaking speed
Test Scenario • Lip reading application • A user is making a phone call • The user’s smartphone is connected to an AP • AP collects CSI streams by sending ICMP requests • We use WiTalk to infer user’s speaking from CSI streams to verify the efficiency of our WiTalk
Test Bed • Tested on channel 36 at 5.180GHz • Context variance • Two model of smartphones • Three users • Five smartphone locations • Two AP locations • A set of 12 syllables • Repeated ten times for each context • A set of sentences from 1 word to 6 words • Repeated five times for each context
Results • 92.3% accuracy same context, 12 syllables • 82.5% accuracy mixed context, 12 syllables
Results • Sentence Detection Accuracy • 74.3% accuracy sentences up to six words
Contributions • We propose WiTalk, a feasible context-free fine-grained motion sensing solution by using WiFi CSI dynamics. • We verify the feasibility of WiTalk by applying it to the lip reading scenario
Questions Thank you!