39:14·N.Yu et al. evaluated the performance of OGesture on different persons,different body parts,and different environments. For efficiency,we evaluated the processing time of the system and the impact on co-existing WiFi users. 5.5 Effectiveness We first verified our assumptions that different pairs of antennas receive different magnitudes of static com- ponents as described in Section 3.4.Figure 10(a)shows the raw CSI data pattern of pushing hand in our 180 subcarriers of which the first 60 subcarriers correspond to the first transmitting antenna.We observe that dif- ferent antenna pairs have different powers.The power is mainly from the magnitudes of static components because of the small reflected area of our small hand.Therefore,we can use the pair of antenna that has the biggest magnitudes of static components to remove the impact of CFO while keeping the phase information of hand movement.We also observe that the magnitude of the static component is more than ten times higher than that of the dynamic component combining Figure 10(a)and 10(b)which is mainly the magnitude of dynamic component. The effectiveness of QGesture is evaluated in both LOS scenario and NLOS scenario in a small conference room as shown in Figure 11.In both scenarios,the user performs gesture at the position marked with a star.We change the distance D,,between the user and receiver and the distance D,between the transmitter and receiver, to evaluate the performance of OGesture in different cases. 3.8 3.5 25 0 90 120 150 180 30 12 150 Subcarrier index Subcarrier index (a)CSI time series pattern of all pairs of antennas. (b)Variance of different subcarriers. Fig.10.Example of different magnitudes of static component on different subcarriers. 1D Pushing Distance Measurement Accuracy:QGesture can measure the distance of hand movement with a 90th percentile measurement error of less than 4 cm at a distance of 1 meter.To verify the performance of our phase correction algorithm,we use different methods to remove the impact of CFO.One is that we chose the subcarrier that has the largest amplitude as reference subcarrier(Largest-PCI),and the other is that we randomly selected the reference subcarrier (Random-PCI).Moreover,we use Hilbert transformation on the CSI stream after PCA processing to get the phase information to measure the moving distance(Hilbert-PCA).Figure 12(a)shows the distance measurement error CDF for a volunteer pushing 80 times for a distance of 40 cm,which is around the longest pushing distance of an adult,at a distance of 1 meter to the receiver.We observe that the average distance measurement error is 2.21 cm,while for 90%cases the error is smaller than 4 cm using Largest-PCI. However,the average distance measurement error is 4.16 cm using Hilbert-PCA,because the phase information given by Hilbert transformation is not as accurate as that given by our phase correction algorithm.In addition, the average distance measurement error is 7.42 cm using Random-PCI,this is because that randomly selecting subcarrier as reference subcarrier may also remove the phase information caused by hand movement.For shorter pushing distances as shown in Figure 12(b),such as 10 cm,the average measurement error reduces to less than 1 Proceedings of the ACM on Human-Computer Interaction,Vol.1,No.4,Article 39.Publication date:March 2018.39:14 • N. Yu et al. evaluated the performance of QGesture on different persons, different body parts, and different environments. For efficiency, we evaluated the processing time of the system and the impact on co-existing WiFi users. 5.5 Effectiveness We first verified our assumptions that different pairs of antennas receive different magnitudes of static com￾ponents as described in Section 3.4. Figure 10(a) shows the raw CSI data pattern of pushing hand in our 180 subcarriers of which the first 60 subcarriers correspond to the first transmitting antenna. We observe that dif￾ferent antenna pairs have different powers. The power is mainly from the magnitudes of static components because of the small reflected area of our small hand. Therefore, we can use the pair of antenna that has the biggest magnitudes of static components to remove the impact of CFO while keeping the phase information of hand movement. We also observe that the magnitude of the static component is more than ten times higher than that of the dynamic component combining Figure 10(a) and 10(b) which is mainly the magnitude of dynamic component. The effectiveness of QGesture is evaluated in both LOS scenario and NLOS scenario in a small conference room as shown in Figure 11. In both scenarios, the user performs gesture at the position marked with a star. We change the distance Du between the user and receiver and the distance Dt between the transmitter and receiver, to evaluate the performance of QGesture in different cases. (a) CSI time series pattern of all pairs of antennas. 0 30 60 90 120 150 180 Subcarrier index 0 2 4 6 8 10 12 14 Variance (b) Variance of different subcarriers. Fig. 10. Example of different magnitudes of static component on different subcarriers. 1D Pushing Distance Measurement Accuracy: QGesture can measure the distance of hand movement with a 90th percentile measurement error of less than 4 cm at a distance of 1 meter. To verify the performance of our phase correction algorithm, we use different methods to remove the impact of CFO. One is that we chose the subcarrier that has the largest amplitude as reference subcarrier (Largest-PCI), and the other is that we randomly selected the reference subcarrier (Random-PCI). Moreover, we use Hilbert transformation on the CSI stream after PCA processing to get the phase information to measure the moving distance (Hilbert-PCA). Figure 12(a) shows the distance measurement error CDF for a volunteer pushing 80 times for a distance of 40 cm, which is around the longest pushing distance of an adult, at a distance of 1 meter to the receiver. We observe that the average distance measurement error is 2.21 cm, while for 90% cases the error is smaller than 4 cm using Largest-PCI. However, the average distance measurement error is 4.16 cm using Hilbert-PCA, because the phase information given by Hilbert transformation is not as accurate as that given by our phase correction algorithm. In addition, the average distance measurement error is 7.42 cm using Random-PCI, this is because that randomly selecting subcarrier as reference subcarrier may also remove the phase information caused by hand movement. For shorter pushing distances as shown in Figure 12(b), such as 10 cm, the average measurement error reduces to less than 1 Proceedings of the ACM on Human-Computer Interaction, Vol. 1, No. 4, Article 39. Publication date: March 2018