QGesture:Quantifying Gesture Distance and Direction with WiFi Signals.39:13 Receiver A Sender Recevier Sender (a)1D Scenario. (b)2D Scenario. Fig.9.Evaluation scenario in small conference room. receivers were 1 meter and 1.2 meters,respectively.We use an asymmetry setup in 2D tracking for asymmetry settings usually provide better performances. During the experiments,we used the similar WiFi channel settings as our campus network.If not specified, WiFi channel 165(f=5.825 GHz)with 40MHZ bandwidth was used.We chose this channel for the shorter wave- length in 5 GHz bands which leads to better sensitivity to hand movements.On average,there were 5 other devices using the same channel in the campus network when we performed the experiment.Hence,QGesture is robust to interference from other WiFi devices using the same channel. 5.3 Experimental Methodology Our experimental data was collected under IRB approval(IRB#15-1042).Five volunteers were involved in the data collection process during a period of three weeks.The volunteers were 3 males and 2 females with ages between 22-25.All volunteers were graduate students with engineering backgrounds.The volunteers were given short 10-minutes introductions of the systems and instructions of the experiments before the experiment. For each experiment,the volunteers were allowed to practice a few times to ensure that they correctly followed the instructions and the gesture recognition system was operating correctly. During the experiments,volunteers were instructed to push their hand normally and naturally either standing close to the receiver or sitting on a chair close to the receiver.If not specified,the distance between the volunteer to the receiver is 1 meter.In the 1D scenario,the volunteers were instructed to push their hand along the same line of the transmitter and receiver.In the 2D scenario,the volunteers were instructed to push along different trajectories,such as a straight line with an angle of 30 degrees,45 degrees,and 60 degrees towards the transmitter. If not specified,each volunteer was instructed to push his/her hand for 50 times for each experiment.The ground truth of the movement distance was measured with a ruler placed along the arm of the volunteer.When the subject was conducting the experiment,other subjects and the experiment coordinator could be in the same room observing the movement and reading the ground truth of the movements. 5.4 Evaluation Metrics We evaluated QGesture from the following three perspectives:effectiveness,robustness,and efficiency.For effectiveness in the 1D scenario,we evaluated the movement distance measurement accuracy at different oper- ational distances.For effectiveness in the 2D scenario,we evaluated the movement direction and distance mea- surement when the arm is moving along a line with a predefined angle with respect to the sender and receiver. We also evaluated the performance of preamble detection in terms of recognition accuracy and False Positive Rate(FPR).The recognition accuracy for preamble detection is defined as the number of detected preambles divided by the total number of preambles performed.The FPR for preamble detection is defined as the number of falsely detected preambles divided by the number of non-preamble activities performed.For robustness,we 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 • 39:13 Recevier Sender (a) 1D Scenario. Receiver A Sender Receiver B (b) 2D Scenario. Fig. 9. Evaluation scenario in small conference room. receivers were 1 meter and 1.2 meters, respectively. We use an asymmetry setup in 2D tracking for asymmetry settings usually provide better performances. During the experiments, we used the similar WiFi channel settings as our campus network. If not specified, WiFi channel 165 (f =5.825 GHz) with 40MHZ bandwidth was used. We chose this channel for the shorter wave￾length in 5 GHz bands which leads to better sensitivity to hand movements. On average, there were 5 other devices using the same channel in the campus network when we performed the experiment. Hence, QGesture is robust to interference from other WiFi devices using the same channel. 5.3 Experimental Methodology Our experimental data was collected under IRB approval (IRB#15-1042). Five volunteers were involved in the data collection process during a period of three weeks. The volunteers were 3 males and 2 females with ages between 22 – 25. All volunteers were graduate students with engineering backgrounds. The volunteers were given short 10-minutes introductions of the systems and instructions of the experiments before the experiment. For each experiment, the volunteers were allowed to practice a few times to ensure that they correctly followed the instructions and the gesture recognition system was operating correctly. During the experiments, volunteers were instructed to push their hand normally and naturally either standing close to the receiver or sitting on a chair close to the receiver. If not specified, the distance between the volunteer to the receiver is 1 meter. In the 1D scenario, the volunteers were instructed to push their hand along the same line of the transmitter and receiver. In the 2D scenario, the volunteers were instructed to push along different trajectories, such as a straight line with an angle of 30 degrees, 45 degrees, and 60 degrees towards the transmitter. If not specified, each volunteer was instructed to push his/her hand for 50 times for each experiment. The ground truth of the movement distance was measured with a ruler placed along the arm of the volunteer. When the subject was conducting the experiment, other subjects and the experiment coordinator could be in the same room observing the movement and reading the ground truth of the movements. 5.4 Evaluation Metrics We evaluated QGesture from the following three perspectives: effectiveness, robustness, and efficiency. For effectiveness in the 1D scenario, we evaluated the movement distance measurement accuracy at different oper￾ational distances. For effectiveness in the 2D scenario, we evaluated the movement direction and distance mea￾surement when the arm is moving along a line with a predefined angle with respect to the sender and receiver. We also evaluated the performance of preamble detection in terms of recognition accuracy and False Positive Rate (FPR). The recognition accuracy for preamble detection is defined as the number of detected preambles divided by the total number of preambles performed. The FPR for preamble detection is defined as the number of falsely detected preambles divided by the number of non-preamble activities performed. For robustness, we Proceedings of the ACM on Human-Computer Interaction, Vol. 1, No. 4, Article 39. Publication date: March 2018