1. WiTrace: Centimeter-Level Passive Gesture Tracking Using WiFi Signals
Lei Wang*, Ke Sun*, Haipeng Dai*, Alex X. Liu*^, Xiaoyu Wang*
Good morning everyone. My name is Lei Wang, I am a Phd student from Nanjing University.
Thanks for deriving this opportunity to talk about our work,”WiTrace: Centimeter-Level Passive Gesture Tracking Using WiFi Signals”.
The coauthor ke sun, Haipeng Dai, xiaoyu wang and Alex X. Liu are from Nanjing university and Michigan State University.
Nanjing University*, Michigan State University^
SECON'18 June 13th, 2018

2. Motivation Gesture tracking inspires various applications.
Tracking with WiFi is superior
Ubiquitous: almost everywhere.
Non-invasive: not wearing/carrying any devices and protect privacy.
Not limited: lighting condition or room layout.
Selecting menu
Playing game
AR assistance
VR assistance
Gesture tracking is a basic human-computer interaction mechanism.
It inspires various applications, [click]such as selecting television menu,[click]playing game.
Additionally, gesture tracking can be used to interact with [click]AR and [click]VR devices.
[Click] Existing tracking methods include camera, computer vision, inertial sensors, RF radars and ultrasonic wave.
Comparing to them, tracking with wifi is superior since WiFi devices are ubiquitous, non-invasive and not limited by lighting condition or room layout.

3. Motivation They have either limited range or tracking resolution !
Google project soli
Mobicom'16 LLAP
MobiHoc'17 Widar
NSDI ’14,WiTrack
NSDI ’15,WiTrack2.0
FMCW signal need a high bandwidth of 1.79 GHz !
A bunch of works on wireless passive tracking have been published in recent years, like Soli based on 60G signal, LLAP based on ultrosonic wave and widar based on wifi.
[Click] These works are either not suitable to serve as a remote control for smart home or limited by working range.
[Click] Based on RF signal, WiTrack and Witrack 2.0 have excellent tracking performance.
[Click] However, these two systems rely on FMCW signal, which need a ultral bandwith more than 1 Ghz .
[Click] On the other hand, there emerge some wireless sensing works using WiFi devices, such as WiKey, WiFinger and WiGest.
[Click] Although these works leverage some wifi characteristics , they mainly focus on trainning-based activity recognition, yet not tracking.
So, we should design a scheme solving all above limitations.
Mobicom'15 WiKey
UbiComp'16 WiFinger
INFOCOM '15 WiGest
Though using Wi-Fi, these solutions focus on training-based activity recognition, yet not tracking.

4. Problem Statement Can we build a gesture tracking system:
Using WiFi signals?
With high precision
With large working range
Based on previous works, we need to propose a moving gesture tracking system satisfying three conditions, namely, using wifi sinals,
Tracking with high precision, and having large working range.

5. CSI Phase Model Challenge-1: Solution: Advantage:
What characteristics of WiFi can be leveraged to achieve cm-level tracking precision?
Solution:
CSI phase
Advantage:
CSI provides more information than other WiFi characteristics (RSSI).
CSI Phase has higher precision over CSI amplitude.
Before design our these system, we should consider the first challenge.
That is what characteristics of wifi can be used to achieve cm-level precision.
As we know, some existing work rely on RSSI or CSI amplitude to track object.
In our work, we choose CSI phase of wifi signal to achieve tracking performance.
This is because CSI is more quantifiable and provide more information comparing to RSSI.
Additionally, CSI phase can give more fine grained tracking performance than CSI amplitude.

6. CSI Phase Model Illustration of multiple paths
Channel State Information can describe the activity information in the environment well. For brevity, we denote it as CSI. We then build a CSI Phase model as shown in the figure.
There are multiple paths where signals propagate from the transmitter to the receiver. As a result, the CSI of the wireless channel at time and frequency is the superimposition response of all paths.
All of the paths can be divided into static paths, e.g. wall and LoS path, and dynamic paths e.g. hand.
Our goal is to measure the phase changes of the dynamic path caused by hand movement, and thereby track hand in the air.
Illustration of multiple paths

7. 1-D Tracking Denoise the CSI signal Detect the movement Hampel filter
Average moving filter
Detect the movement
After we receive the raw CSI signal. We preprocess the signals as following steps.
First, we apply the Hampel filter to filter out the I/Q component outliers which have significant different values from others. The green curve in the left Figure shows the results afte Hampel filter.
Second, we utilize the moving average low-pass filter to further remove high frequency noises. The black curve in the left Figure shows the result.
Before measuring the movement, our system needs to detect the start of the movement. We apply the sliding window to compute the variance of the amplitude continuously.
As shown in the right Figure, the variance in static period is much smaller than the variance in dynamic period.
So the movement period can be easily detected by using empirical threshold.

8. 1-D Tracking Challenge-2:
How to seperate the phase changes caused by moving hands from CSI values due to other environments?
Existing work:
DDBR: low surrounding noise and can hardly detect slow movement.
LEVD: difficult to reliably detect the local maximum and minimum points
In reality, it is challenging to remove static vector from the CSI measurement. On one hand, the static vector mainly caused by static reflectors is much stronger than dynamic vector caused by hand.
On the other hand, static vector may change slowly with the moving hand due to blocking other reflectors and the slow movement of other body parts.
Additionally, even though dynamic vector of hand dominates the variation of CSI, SNR will degrade with distance between hand and receiving end.
There are some existing works to separate static vector from dynamic vector. The mmWave radio system, mTrack uses DDBR to remove the static vector. Compared to the mmWave signal, CSI signal is more susceptible to ambient noise. However, DDBR requires low surrounding noise and can hardly detect slow movement.
Another ultrasonic system, LLAP, applies a heuristic algorithm called LEVD to estimate the static vector. However, it is difficult to reliably detect the local maximum and minima points.

9. 1-D Tracking Extracting Static Component (ESC):
Find alternate maximum and minimum points that are lzrger than the emperical threshold.
STFT to derive the instaneous Doppler frequency shift.
Remove extreme points smaller than threshold.
Average adjacent two points to derive the static value.
To measure the static vector of CSI, we first find alternate maximum and minimum points that are larger than the emperical threshold.
Then, we use stft method to derive the instaneous doppler frequency shift related to the temporal threshold.
We then remove extreme points smaller than the threshold and average adjacent two points to derive the static value.
The dynamic vector can be consequently estimated by removing the static vector.

10. 1-D Tracking ESC vs. LEVD:
ESC improves the robustness to small ambinent noise than LEVD
ESC is more sensitive to small body movement than LEVD
As shown in leftmost Figure , ESC improves the accuracy to detect the hand movement and avoid the small noise induced by ambient environment within the first 0.15 seconds.
And ESC outperforms another method LEVD.
Then , we remove the static vector and extract the dynamic vector to get the corresponding phase change as shown in right-most Figure.
ESC vs LEVD
I/Q trace of
raw CSI
I/Q trace of
dynamic vector

11. 2-D Tracking Challenge-3:
How to estimate the initial position of hand in 2-D space?
Existing work:
mTrack: discrete beam scanning mechanism to pinpoint the object's initial localization.
LLAP: IDFT to process CFR signals for all subcarriers to estimate the absolute position.
Basic idea:
Two preamble gestures to measure the initial position of hand.
The trajectory of hand in 2D space is not only determined by the path length change of movement but also by its initial position.
The incorrect initial position of hand will severely affect the tracking result.
So, how to estimate the initial position of hand in 2-D space.
There are some existing works to estimate the initial position.
m-track uses a discrete beam scanning mechanism to pinpoint the object’s localization.
They use two rx antennas of narrow beamwidth to determine the intersection of two receiving beams.
Llap uses IDFT to process CFR signals for all subcarriers to estimate the absolute initial position.
Comparing to these works, our basic idea is to use two preamble gestures to measure the initial position of hand.

12. 2-D Tracking Initial Position Estimation
User push hand along x-axis and y-axis;
Set the grid as the candidate initial position;
Calculate the tracking trajectory for two receivers based on the initial position and path change for two directions.
To determine the initial position, we firstly ask the user to push hand along x-axis and y-axis for limited length.
We set any grid in the limited range as the candidate initial position and calculate the tracking trajectory for two receivers based on each candidate initial position and path length change for two directions.

13. 2-D Tracking Initial Position Estimation
Find N candidate positions which have the N top smallest deviations and for x-axis and y-axis, respectively.
Calculate N*N distance matrix , where
Find the smallest element in the matrix and average the coordinate value.
Then, we find the candidate positions which have N top smallest deviations along x-axis and y-axis, respectively.
We calculate any two candidate positions’ Euclidean distances to constitute a distance matrix.
Here, we combine two directions to find the smallest element and average the coordinate values.

14. 2-D Tracking Initial Position Estimation
[Click]Just like the figure, we use the brightness to represent the trajectory deviation of various axis for various initial positions.
[Click]The darkest points mean the estimated initial positions.
[Click]We then average these two points’ positions to derive the final result.

15. 2-D Tracking Successive 2-D tracking Trajectory Correction
Estimate the initial hand position
Solve two equations corresponding to two receivers
Trajectory Correction
Kalman filter based on CWPA model
For 2D tracking, we use one transmitter and two receivers to track hand in two-dimensional space.
By solving two equations, the realtime position of hand can be located, and the next successive position will be derived from this iterative method.
We then use the kalman filter based on cwpa model to further improve the tracking accuracy.

16. Implementation Devices Parameters: 3 USRP-N210
2 links (1 per receiver)
Parameters:
20 MHz bandwith
64 CSI subcarriers
Central frequency at 2.4GHz
Tx power: 20dBm
1D scenario
We build the system with 3 USRP equipped with SBX board.
One is transmitter and others are receivers. So there are 2 links in total.
The transmitting USRP sends OFDM frames with bandwidth of 20 MHz at 2.4 GHz.
There are 64 subcarriers in each transmitted frame, among which 48 subcarreiers are for data, 4 subcarriers are for pilot.
Each receiver collects CSI measurements at a rate of 20 M samples per second using a laptop.
2D scenario

17. Experiment 1-D tracking performance
WiTrace achieves average error of 1.46 cm and 4.99 cm with and without the plank.
WiTrace achieves average error of 3.75 cm and 2.51 cm for omnidirectional antenna and directional antenna.
ESC achieves better performance than other algorithms.
[Click]For 1-D tracking, our system achieves a median tracking error of less than 2cm and 5cm, with and without thick plank between the transmitter and receiver.
[Click]WiTrace achieves average error of less than 4cm and 3 cm for omnidirectional and directional antennas in the range of 2 m.
[Click]By using ESC, WiTrace achieves improvement over other algorithms as shown in the last Figure.This is because ESC algorithm is less susceptible to noise and robust for different distances.

18. Experiment 1-D tracking performance
WiTrace is robust to background activities which are 2 m away from the receiver for different users.
WiTrace achieves average tracking error of 6.46 cm and 3.80 cm while pushing hand at different heights and walking around, respectively.
[Click]WiTrace is robust to background activities which are 2 m away from the receiver for different users. The average errors for“Normal”,“Office”, and“Walk” are all less than 3cm, respectively.
[Click]WiTrace achieves average tracking error for less than 7cm and 4 cm while pushing hand at various heights and walking at different distances, respectively.

19. Experiment 2-D tracking performance
WiTrace achieves average 3.91 cm estimated error with the template,and average cm error without template for intial position estimation.
[Click]For 2D tracking, WiTrace achieves average 4 cm estimated error with the template, and average 10 cm error without template.
The larger error is because the user's hand movement without template is more random than pushing along the template.

20. Experiment 2-D tracking performance
WiTrace achieves an average tracking error of 2.09 cm for three shapes' trajectory (i.e., rectangle, triangle, and circle).
[Click]WiTrace achieves an average tracking error of about 2 cm for three shapes' trajectories.
It indicates that Witrace can track various trajectories with high precision and Kalman filter method can improve the tracking accuracy effectively. .

21. Conclusions
WiTrace achieves high accuracy gesture tracking using WiFi signals.
We propose a novel scheme based on two preamble gestures to measure the initial position of hand.
We implement WiTrace on USRP.
[Start] As a conclusion, in this work, we propose a CSI-Phase model, ESC algorithm, a novel scheme based on two preamble gestures to measure the initial position of hand in 2-D space, and implement a fine-grained tracking system with centimeter-level accuracy.

22. Q&A
[Start]. That's all. Thanks for listening.
Any questions?