QGesture:Quantifying Gesture Distance and Direction with WiFi Signals.39:9 pass moving average filter with window size of 80 samples to smooth the CSI magnitude,as well as the residual phase noises After the phase correction and magnitude correction,the CSI value of highly noisy subcarriers could still be corrupted.So we use subcarrier selection [20]and linear regression to reduce the error. 4 SYSTEM DESIGN After removing the noises in CSI measurements,QGesture first uses the LEVD algorithm to remove the static component in the complex plane.We then measure the hand movement distance and direction based on the phase-distance relationship introduced in Section 3.1. 4.1 Real-world CSI Measurements To better understand the CSI measurements provided by COTS devices,Figure 5 plots the CSI value captured by Intel 5300 network card.After denoising,the I/Q waveforms from COTS devices fit our theoretical model quite well.As the user pulls back his hand by 40 cm,we observe around 16 peaks in the waveform,which indicates that the phase is changed by 32m.Using the wavelength of 5.15 cm at 5.825 GHz,we get a path length change of 82.4 cm,which is very close to our model as we have a=2 in this case.Furthermore,we observe that the phase is reducing(CSI values circling clockwise as in Figure 5(b)),which indicates that the user is pulling back. We further observe that the real-world CSI values deviate from the theoretical model in two aspects.First, the static component is not a constant where the center of the circles in Figure 5(b)slowly changes.This is mainly due to the slow movements of the other body parts,such as the arms or the torso,of the user.Second, the magnitude of the dynamic component also changes.This is due to the reduction of strength of the reflected signal when the hand moves away from the transmitter/receiver.The slowly changing static component and reflected signal strength make it challenging to measure the phase of the dynamic component.For example,if we use a constant static component estimation,e.g,with I component equal to 2,the last few small peaks in Figure 5(a)could be ignored and we will underestimate the movement distance. 4.2 Removing Static Components QGesture uses a Local Extreme Value Detection algorithm(LEVD)to trace the slowly changing static compo- nent.The LEVD algorithm first initializes the static component estimation S(t)as the long-term average,e.g.. average value over 2 seconds,of the CSI real part or imaginary part.As the channel coherent time for our WiFi scenario is about 10 ms,averaging over a time period of 2 seconds is enough to smooth out the channel varia- tions.The algorithm uses an empirical threshold of T,which is determined by experiments,as shown in Section 5.5,to detect local maxima and minima.Once the CSI value deviates from its mean value by more than T,the algorithm starts detecting local maxima and minima.The local maxima and minima must satisfy the following two properties:1)Local maxima must be at least larger than the current static component estimation S(t)by the value of T.Similarly,local minima must be smaller than S(t)-T.2)Local maxima and minima must appear alternately.If there are two consecutive local maxima/minima,we only retain the larger/smaller one. While detecting the local extrema,we update the static component estimation S(t)dynamically by setting it to be the average of the last pair of local maximum and minimum values.In this way,LEVD is able to trace the slowly changing static component.Figure 6(a)shows the local extrema detected by LEVD,which precisely indicates the cycles of the waveform.After removing the estimated static component,LEVD gives a good estimation of dynamic component of the CSI waveform in Figure 6(a).The result of LEVD is better than simply removing the average of the CSI waveform.Figure 6(b)shows the distance estimation of LEVD and the simple average-removal algorithm for the changing path length of 80 cm.Using Eq.(2),we find that the pushing distance estimation of LEVD is 40.17 cm,while the distance estimation of simple average-removal is 33.6 cm.We observe that the Proceedings of the ACM on Human-Computer Interaction,Vol.1,No.4,Article 39.Publication date:March 2018.QGesture: Quantifying Gesture Distance and Direction with WiFi Signals • 39:9 pass moving average filter with window size of 80 samples to smooth the CSI magnitude, as well as the residual phase noises. After the phase correction and magnitude correction, the CSI value of highly noisy subcarriers could still be corrupted. So we use subcarrier selection [20] and linear regression to reduce the error. 4 SYSTEM DESIGN After removing the noises in CSI measurements, QGesture first uses the LEVD algorithm to remove the static component in the complex plane. We then measure the hand movement distance and direction based on the phase-distance relationship introduced in Section 3.1. 4.1 Real-world CSI Measurements To better understand the CSI measurements provided by COTS devices, Figure 5 plots the CSI value captured by Intel 5300 network card. After denoising, the I/Q waveforms from COTS devices fit our theoretical model quite well. As the user pulls back his hand by 40 cm, we observe around 16 peaks in the waveform, which indicates that the phase is changed by 32π. Using the wavelength of 5.15 cm at 5.825 GHz, we get a path length change of 82.4 cm, which is very close to our model as we have a = 2 in this case. Furthermore, we observe that the phase is reducing (CSI values circling clockwise as in Figure 5(b)), which indicates that the user is pulling back. We further observe that the real-world CSI values deviate from the theoretical model in two aspects. First, the static component is not a constant where the center of the circles in Figure 5(b) slowly changes. This is mainly due to the slow movements of the other body parts, such as the arms or the torso, of the user. Second, the magnitude of the dynamic component also changes. This is due to the reduction of strength of the reflected signal when the hand moves away from the transmitter/receiver. The slowly changing static component and reflected signal strength make it challenging to measure the phase of the dynamic component. For example, if we use a constant static component estimation, e.g., with I component equal to 2, the last few small peaks in Figure 5(a) could be ignored and we will underestimate the movement distance. 4.2 Removing Static Components QGesture uses a Local Extreme Value Detection algorithm (LEVD) to trace the slowly changing static compo￾nent. The LEVD algorithm first initializes the static component estimation S (t) as the long-term average, e.g., average value over 2 seconds, of the CSI real part or imaginary part. As the channel coherent time for our WiFi scenario is about 10 ms, averaging over a time period of 2 seconds is enough to smooth out the channel varia￾tions. The algorithm uses an empirical threshold of T , which is determined by experiments, as shown in Section 5.5, to detect local maxima and minima. Once the CSI value deviates from its mean value by more than T , the algorithm starts detecting local maxima and minima. The local maxima and minima must satisfy the following two properties: 1) Local maxima must be at least larger than the current static component estimation S (t) by the value of T . Similarly, local minima must be smaller than S (t) −T . 2) Local maxima and minima must appear alternately. If there are two consecutive local maxima/minima, we only retain the larger/smaller one. While detecting the local extrema, we update the static component estimation S (t) dynamically by setting it to be the average of the last pair of local maximum and minimum values. In this way, LEVD is able to trace the slowly changing static component. Figure 6(a) shows the local extrema detected by LEVD, which precisely indicates the cycles of the waveform. After removing the estimated static component, LEVD gives a good estimation of dynamic component of the CSI waveform in Figure 6(a). The result of LEVD is better than simply removing the average of the CSI waveform. Figure 6(b)shows the distance estimation of LEVD and the simple average-removal algorithm for the changing path length of 80 cm. Using Eq. (2), we find that the pushing distance estimation of LEVD is 40.17 cm, while the distance estimation of simple average-removal is 33.6 cm. We observe that the Proceedings of the ACM on Human-Computer Interaction, Vol. 1, No. 4, Article 39. Publication date: March 2018