1. Context-Free Fine-Grained Motion Sensing using WiFi
Good morning, everyone, I am Xiaoqun Yuan, I’m glad to have this chance to share our work. It is joint work with my collergues changlai du, wenjing lou and tom hou. The title of our work is Context-Free Fine-Grained Motion Sensing using WiFi.
Changlai Du, Xiaoqun Yuan, Wenjing Lou, Thomas Hou
Virginia Tech
Wuhan University
June. 13, 2018

2. 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]
Recently, more and more wireless devices connect to internet. Some reports estimate that more than 20 billion wireless sensors will be in use by 2020
and about 99% of then connect to internet.
[1] Gartner,
[2] The Future of Wireless.

3. Transferring Data
The most important job of wireless signals is to transfer data among transceivers.

4. Discover the Sensing World
On the other hand, it is possible to discover the sensing world with these wireless signals.
For example,
to find the location of a mobile device,
find the location of people though the wall;
to recognize human activities;
to measure vital signs like breathing rate and heartbeat rate;
to recover the input pin on smartphones;
to detect human emotion;

5. 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.
motive by these applications, we completed our work: WiTalk,
which is a CSI-based fine-grained motion sensing scheme,
It has the key feature of one-time training and resilient to context change.
This work is accepted to SECON 18.

6. 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
In fact, CSI-based motion sensing is a hot topic and be wild used in different fields,
such as human localization,
activity detection,
human authentication,
health care
and motion detection.
We also find these works focused on two performance: context and grained.
So we use grained as x-axis and context as y-axis.
These works can be classified into four quadrants.

7. Research Positioning 2 1
From this figure, we can find E-eyes is in the third quadrant;
CARM and WifiU are classified in the quadrant 2;
Fine-grained solutions like WindTalker WiKey and WiHear are divided into quadrant 4.
Yes, there are no solutions in quadrant one with the context-free fine-grained. That’s why we propose our motion sensing scheme 3 4

8. Channel State Information(CSI)
As mentioned above, our Witalk is based on CSI, but what is the CSI.
Suppose we have the transmitted signals X and the received singals Y in frequency domain.
We have an equation of Y=H*X.
here, H is the channel frequency response(CFR).
Assume OFDM is used to collect CFR values on 30 subcarriers.
For a N * M MIMO connection, we can achieve a N * M * 30 matrix.
This matrix is called CSI matrix.
And the time series of CSI matrix is called CSI streams.

9. 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
But Why we can detect human motion using CSI?
A short answer is that Human motion changes the value of CSI,
so we can infer human motion from CSI changes.
Theoretically, it can be explained by the Csi path-phase model, as shown in this figure.
In this figure, Human Motion cause some dynamic multipath components.
If the dynamic path and static path are in-phase, then it is a constructive factor. Otherwise, it is a destructive factor.
So we can say that the dynamic path phase changes is the reason of CSI changes.

10. 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
This problem is briefly described here.

11. 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
Though CSI streams can be used to infer user’s speaking, there some challenges we have to deal with.
The first is Fine-grained motion caused CSI variance is very tiny.
These useful variances can easily be buried in noise and interference. So we need some Efficient CSI stream denoising methods.
The other challenge is CSI waveforms change with context.
That is,
Training per context in previous works are not acceptable for eavesdropping.
We need to find intrinsic features in CSI dynamics correlated to fine-grained motion only.

12. Workflow
To deal with these two challenges, we design our WiTalk, the workflow is shown as this figure.
It includes six components,
the CSI collection,
interferences elimination,
feature extraction,
segmentation,
classification and
error correction.
In this paper, we focus on interferences elimination and feature extraction.

13. CSI Denoising Band Pass Filter 3-order Butterworth filter
Remove high frequency noise
Remove human breathing interference( Hz)
Keep mouth movement caused frequencies(2-5Hz)
To eliminate the interferences, we first use a Band Pass Filter to denoise the CSI streams.
To this end, 3-order Butterworth filter is used to
remove high frequency noise and human breathing interference ( Hz) .
keep mouth movement, from two to five Hz

14. CSI Denoising PCA Based Filtering
CSI streams of different subcarriers correlate their variations
Chose the second principal component as the filter result
Then we use a PCA based method to further regulate the CSI streams.
This is because that CSI streams of different subcarriers correlate their variations.
PCA can extract the principal components with largest variance.
Since the first component may contain large interference caused by hardware,
we choose the second principal component as the filter results.
The results after applying Band Pass Filter and PCA denoising are shown as these figures.

15. 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
In the feature extraction step, we use spectrogram for different syllables to extract the features of CSI streams.
It is because that Spectrogram is stable for different contexts and it has been proved using the CSI-Speed model.
However, due to the computation costs for classification, it is infeasible to use spectrogram directly.
On the other hand, using the contour lines as the features can reduce computation cost.
So we also use dynamic time warping to deal with different speaking speed.

16. 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
In our paper, we test our WiTalk in lip reading applications.
In this test scenario, an user is making a phone call
and his smartphone connects to an access point(AP).
This AP then collects the CSI streams by sending ICMP requests to the smartphone.
Then we use WiTalk to infer the user’s speaking content from the CSI streams to verify the efficiency of our WiTalk.

17. 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
Our test is carried out in a typical office environment and all experiments are performed on channel 36 at 5.180GHz.
We collect data with three volunteers using two model of smartphones at five smartphone locations and two AP locations.
The volunteers are asked to read a set of 12 syllables 10 times for each context and a set of short sentences no more than six words for 5 times.

18. Results 92.3% accuracy same context, 12 syllables
82.5% accuracy mixed context, 12 syllables
We first train and test the classifier in the same context with the same volunteer at the same location.
The results show confusion matrix with more than 92% of the average detection accuracy
Then we mix the data collected from different users, different transmitters at different locations for both training and testing.
Our WiTalk achieves more than 82% of the average detection accuracy.

19. Results Sentence Detection Accuracy
74.3% accuracy sentences up to six words
As to sentence detection, the test results show that
the average sentence detection accuracy is more than 74% with context-based error correction,
performance better than that without context error correction.

20. 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

21. Questions
Thank you!