TRANSACTIONS ON MOBILE COMPUTING,VOL.17,NO.10,OCTOBER 2018 2 Table 1 Push Comparison of different WiFi-based systems NLOS A System Object Granularity Range TX&RX LOSB WiDraw 101 Hand 5 cm 0.6m 27 Transmitter Receiver QGesture [13] Hand 5.5cm 2m 3 wdar[11】 Human body 0.83.2m 3 NLOS CY 25 cm Widar 2.0 [12] Human body 75 cm 8m Wall Wikey [3] Gesture Recognition 4m 25 WiFinger [4] Gesture Recognition 14m Wigesture [9] Gesture Recognition 22m 22 WiTrace Gesture 2.09cm 23.5m Figure 1.Illustration of multiple paths WiTrace addresses three critical challenges.The first challenge 2 CSI PHASE MODEL is to achieve cm-level hand tracking accuracy for large range In this section,we describe the theoretical model of Channel State based on WiFi signals.Prior WiFi based tracking scheme uses Information (CSI)regarding dynamic gesture movement.Specific- AOA to track hand with large number of transmitters in the range ally,CSI estimates the channel properties of a communication of 2 feet [10].In contrast,we leverage the fact that the phase link,which is described by channel frequency response (CFR) changes of dynamic component of CSI are proportional to the path for k-th subcarrier frequency fk [23].As a result,CSI of the length changes caused by the object movement.By measuring k-th subcarrier at time t is the superimposition response of all and analyzing the phase changes.WiTrace achieves an average transmission paths [24]: distance error of 1.46 cm when pushing hand for 30 cm in the range of 1.2 m using omnidirectional antennas. The second challenge is to separate the phase changes caused (2πfkd(t)/c+中e ej(f,,(1) by the moving hands from CSI values caused by other environ- ments.The Signal-to-Noise Ratio (SNR),which represents the ratio of the reflecting power of target objects and other static where K is the total number of paths,is the attenuation coefficient of the k-th subcarrier,di(t)is the length of path i,c is objects,attenuates at long distance.As a result,the phase changes caused by the moving hands can be easily contaminated by other the speed of the wireless signal,and o;is the initial phase caused by time delay of the imperfect hardware.Additionally,traditional ambient interference,which means it is challenging to extract the phase changes from mixture signals.To address this challenge, CSI measurements typically have a phase shift of(f,t),which we apply a heuristic algorithm,i.e.Extracting Static Component is caused by residual frequency offset due to non-synchronized- clocks between transceiver pair.In order to rule out the phase (ESC)which lies in its robustness to the ambient interference. errors,we use an external clock [25]to connect the transmitter For In-phase (i.e.I)or Quadrature (i.e.Q)components of CSI. we first find the nearby local maxima and minima using empirical and the receiver in our system. As shown in Figure 1,all of the paths can be divided into threshold.To wipe out those noisy extreme points,we set temporal static paths,e.g.,the wall and LoS path,and dynamic paths e.g., threshold that is determined by the maximal Doppler frequency. The third challenge is to estimate the initial position of hand the hand.For static path i,the length of path di can be considered as fixed during a short period.As a result,Eq.(1)can be rewritten in 2D space.Although we can precisely measure the distance as' changes of hand movements,it is difficult to locate the absolute position of the hand directly without the initial hand location. Existing indoor localization based on WiFi signals [20]-[22]can k(t)=afei(2rd()/+), (2) be used for initial location estimation.However,these systems iEPa only get the coarse location at decimeter level of the human body,which is insufficient for gesture tracking.To address this whereis the sum of CSI for the static paths that are constant challenge,we first estimate the coarse initial hand position based for a short duration,P is the set for the dynamic paths,and on the CSI phase difference of variant subcarriers caused by Ak=c/fr is the wavelength for frequency f. hand movement.This coarse initial position estimation can narrow Suppose we can derive the phase change of path i,i.e..A. down the candidate region for the following fine estimation step where the phase i is i=2mdi(t)/k+i.Thus,the length so that the computation complexity of the fine estimation can be change of dynamic path i is given by: significantly reduced.Then,we utilize the fact that the estimated trajectory would be different for different initial positions.We use the result of two preamble gestures as the fingerprints of different △d,= △p入 (3) 2π initial positions and combine two directions to refine the initial where Ai is the phase change of path i. position estimation.Our approach achieves an average accuracy Finally,our goal is to measure the phase changes of the of 6.23 cm for initial position estimation. dynamic path caused by hand movement,and thereby determine We implemented WiTrace using USRP transceivers.Our ex- the length change of dynamic path to track hand in the air. perimental results show that our approach achieves estimated accuracy of the initial hand position 6.23 cm on average,and tracks the hand movement with mean accuracy of 1.46 cm for ID 3 CSI PHASE BASED DISTANCE MEASUREMENT tracking and 2.09 cm for 2D tracking,respectively.The result also In this section.we propose a method to measure hand movement. shows that WiTrace reaches overall mean direction error of 7.32 Our measurement method contains four steps,as shown in Fig- degrees across five different directions in 2D space case. ure 2.First,we apply the Hampel filter to remove the noise ofTRANSACTIONS ON MOBILE COMPUTING, VOL. 17, NO. 10, OCTOBER 2018 2 Table 1 Comparison of different WiFi-based systems System Object Granularity Range TX&RX WiDraw [10] Hand 5 cm 0.6 m 27 QGesture [13] Hand 5.5 cm 2 m 3 Widar [11] Human body 25 cm 0.8 ∼ 3.2 m 3 Widar 2.0 [12] Human body 75 cm 8 m 2 Wikey [3] Gesture Recognition 4 m 5 WiFinger [4] Gesture Recognition 1 ∼ 4m 5 Wigesture [9] Gesture Recognition ≥ 2m ≥ 2 WiTrace Gesture 2.09 cm ≥ 3.5 m 3 WiTrace addresses three critical challenges. The first challenge is to achieve cm-level hand tracking accuracy for large range based on WiFi signals. Prior WiFi based tracking scheme uses AOA to track hand with large number of transmitters in the range of 2 feet [10]. In contrast, we leverage the fact that the phase changes of dynamic component of CSI are proportional to the path length changes caused by the object movement. By measuring and analyzing the phase changes, WiTrace achieves an average distance error of 1.46 cm when pushing hand for 30 cm in the range of 1.2 m using omnidirectional antennas. The second challenge is to separate the phase changes caused by the moving hands from CSI values caused by other environ￾ments. The Signal-to-Noise Ratio (SNR), which represents the ratio of the reflecting power of target objects and other static objects, attenuates at long distance. As a result, the phase changes caused by the moving hands can be easily contaminated by other ambient interference, which means it is challenging to extract the phase changes from mixture signals. To address this challenge, we apply a heuristic algorithm, i.e. Extracting Static Component (ESC) which lies in its robustness to the ambient interference. For In-phase (i.e. I ) or Quadrature (i.e. Q) components of CSI, we first find the nearby local maxima and minima using empirical threshold. To wipe out those noisy extreme points, we set temporal threshold that is determined by the maximal Doppler frequency. The third challenge is to estimate the initial position of hand in 2D space. Although we can precisely measure the distance changes of hand movements, it is difficult to locate the absolute position of the hand directly without the initial hand location. Existing indoor localization based on WiFi signals [20]–[22] can be used for initial location estimation. However, these systems only get the coarse location at decimeter level of the human body, which is insufficient for gesture tracking. To address this challenge, we first estimate the coarse initial hand position based on the CSI phase difference of variant subcarriers caused by hand movement. This coarse initial position estimation can narrow down the candidate region for the following fine estimation step so that the computation complexity of the fine estimation can be significantly reduced. Then, we utilize the fact that the estimated trajectory would be different for different initial positions. We use the result of two preamble gestures as the fingerprints of different initial positions and combine two directions to refine the initial position estimation. Our approach achieves an average accuracy of 6.23 cm for initial position estimation. We implemented WiTrace using USRP transceivers. Our ex￾perimental results show that our approach achieves estimated accuracy of the initial hand position 6.23 cm on average, and tracks the hand movement with mean accuracy of 1.46 cm for 1D tracking and 2.09 cm for 2D tracking, respectively. The result also shows that WiTrace reaches overall mean direction error of 7.32 degrees across five different directions in 2D space case. Transmitter Receiver Wall Push NLOS C LOS B NLOS A Figure 1. Illustration of multiple paths 2 CSI PHASE MODEL In this section, we describe the theoretical model of Channel State Information (CSI) regarding dynamic gesture movement. Specific￾ally, CSI estimates the channel properties of a communication link, which is described by channel frequency response (CFR) for k-th subcarrier frequency fk [23]. As a result, CSI of the k-th subcarrier at time t is the superimposition response of all transmission paths [24]: −→H(t) k = X K i=1 α k i,te j(2πfkdi(t)/c+φi) ! e jψ(fk,t) , (1) where K is the total number of paths, α k i,t is the attenuation coefficient of the k-th subcarrier, di(t) is the length of path i, c is the speed of the wireless signal, and φi is the initial phase caused by time delay of the imperfect hardware. Additionally, traditional CSI measurements typically have a phase shift of ψ(fk, t), which is caused by residual frequency offset due to non-synchronized￾clocks between transceiver pair. In order to rule out the phase errors, we use an external clock [25] to connect the transmitter and the receiver in our system. As shown in Figure 1, all of the paths can be divided into static paths, e.g., the wall and LoS path, and dynamic paths e.g., the hand. For static path i, the length of path di can be considered as fixed during a short period. As a result, Eq. (1) can be rewritten as: −→Hk (t) = −→Hk st + X i∈Pd α k i,te j(2πdi(t)/λk+φi) , (2) where −→H fk st is the sum of CSI for the static paths that are constant for a short duration, Pd is the set for the dynamic paths, and λk = c/fk is the wavelength for frequency fk. Suppose we can derive the phase change of path i, i.e., ∆ϕi , where the phase ϕi is ϕi = 2πdi(t)/λk + φi . Thus, the length change of dynamic path i is given by: ∆di = ∆ϕiλk 2π (3) where ∆ϕi is the phase change of path i. Finally, our goal is to measure the phase changes of the dynamic path caused by hand movement, and thereby determine the length change of dynamic path to track hand in the air. 3 CSI PHASE BASED DISTANCE MEASUREMENT In this section, we propose a method to measure hand movement. Our measurement method contains four steps, as shown in Fig￾ure 2. First, we apply the Hampel filter to remove the noise of