39:12·N.Yu et al. pause 0.8 1.2 14 Time (seconds) Fig.8.CSI waveform for the punch gesture. punch data,which contains 400 valid extremas for punch and 400 invalid extremas for extrema generated by other activities.The regression model calculates the probability that the detected extrema points belong to a punch action,based on its amplitude and intervals between neighboring extrema.In this way,we can detect punch gesture by searching for two segments with consecutive 5-12 valid extrema,separated by a short pause of 0.2-0.4 seconds.Using these simple criteria,QGesture can efficiently detect a punch or double punch with an accuracy higher than 92%and a false positive rate lower than 3%. The preamble gestures serve for two purposes.First,they mark the starting point of the gesture so that other actions are not confused as gestures.Second,they indicate the user's intension for the following action.For example,we can use a single punch preamble to indicate that the following pushing action is to change the volume of the TV and use a double punch preamble to indicate an action of changing the brightness of the light. Note that we can introduce more gestures,such as snapping fingers,waving hands,as preambles to represent different meaning of the following gesture. 5 IMPLEMENTATION AND EVALUATION 5.1 Implementation We implemented QGesture on COTS WiFi devices.QGesture has three components:A WiFi router(NetGear JR6100)which is configured to send 2,500 UDP packets every second,one or two ThinkPad X200 laptop with Intel 5300 wireless card,and a server for data processing.In the 1D scenario,QGesture use one laptop to collect per frame CSI at a rate of 2,500 samples per second using the Linux CSI tool [12].In the 2D scenario,QGesture collect two separate CSI streams from two laptops placed in a setup as shown in Figure 9(b).The clocks of these two laptops are synchronized by NTP so that we have synchronized timestamp for each CSI measurement.The collected CSI data are forwarded to a server which performs signal processing using MATLAB. 5.2 Experimental Environments Figure 9 shows the experimental environments for the performance evaluation in the 1D scenario and the 2D scenario.We performed the experimental evaluation in three different environments.The first one was a small conference of size 7.5 x 6.5m with a table of 2 x 2m,with the sender/receiver placed on the table.The second one was in the same room as the first one,but the transmitter and the receiver were placed near the wall which introduced rich multipath interferences.The third one was a large lobby area with a size of 20 x 20m.If not specified,most of our experiment was performed in the first environment.If not specified,the sender and the receiver were placed on a table with a height of about 1 meter.In the 1D scenario,the default distance between the sender and the receiver was 1 meter.For 2D hand tracking,the distance between the sender and the two Proceedings of the ACM on Human-Computer Interaction,Vol.1,No.4,Article 39.Publication date:March 2018.39:12 • N. Yu et al. 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 Time (seconds) -6 -4 -2 0 2 4 6 8 CSI magnitude 1 2 pause Fig. 8. CSI waveform for the punch gesture. punch data, which contains 400 valid extremas for punch and 400 invalid extremas for extrema generated by other activities. The regression model calculates the probability that the detected extrema points belong to a punch action, based on its amplitude and intervals between neighboring extrema. In this way, we can detect punch gesture by searching for two segments with consecutive 5–12 valid extrema, separated by a short pause of 0.2–0.4 seconds. Using these simple criteria, QGesture can efficiently detect a punch or double punch with an accuracy higher than 92% and a false positive rate lower than 3%. The preamble gestures serve for two purposes. First, they mark the starting point of the gesture so that other actions are not confused as gestures. Second, they indicate the user’s intension for the following action. For example, we can use a single punch preamble to indicate that the following pushing action is to change the volume of the TV and use a double punch preamble to indicate an action of changing the brightness of the light. Note that we can introduce more gestures, such as snapping fingers, waving hands, as preambles to represent different meaning of the following gesture. 5 IMPLEMENTATION AND EVALUATION 5.1 Implementation We implemented QGesture on COTS WiFi devices. QGesture has three components: A WiFi router (NetGear JR6100) which is configured to send 2,500 UDP packets every second, one or two ThinkPad X200 laptop with Intel 5300 wireless card, and a server for data processing. In the 1D scenario, QGesture use one laptop to collect per frame CSI at a rate of 2,500 samples per second using the Linux CSI tool [12]. In the 2D scenario, QGesture collect two separate CSI streams from two laptops placed in a setup as shown in Figure 9(b). The clocks of these two laptops are synchronized by NTP so that we have synchronized timestamp for each CSI measurement. The collected CSI data are forwarded to a server which performs signal processing using MATLAB. 5.2 Experimental Environments Figure 9 shows the experimental environments for the performance evaluation in the 1D scenario and the 2D scenario. We performed the experimental evaluation in three different environments. The first one was a small conference of size 7.5 × 6.5m with a table of 2 × 2m, with the sender/receiver placed on the table. The second one was in the same room as the first one, but the transmitter and the receiver were placed near the wall which introduced rich multipath interferences. The third one was a large lobby area with a size of 20 × 20m. If not specified, most of our experiment was performed in the first environment. If not specified, the sender and the receiver were placed on a table with a height of about 1 meter. In the 1D scenario, the default distance between the sender and the receiver was 1 meter. For 2D hand tracking, the distance between the sender and the two Proceedings of the ACM on Human-Computer Interaction, Vol. 1, No. 4, Article 39. Publication date: March 2018