QGesture:Quantifying Gesture Distance and Direction with WiFi Signals.39:3 variations without disturbing the small phase changes caused by hand movements.To address this challenge,we carefully analyze the phase offsets in different antenna pairs and design our phase correction algorithm so that phase changes of hand movements can be preserved.Hence,we can determine the movement direction with an accuracy of more than 95% Separate the channel state changes caused by the moving hands from the mixture of changes caused by other body parts:This is particularly important for a gesture recognition system to operate over a long distance, i.e.,several meters,because such system captures both the gesture movements and the environmental dynamics. When the user performs the gesture,their torso and arms also move at the same time,which significantly perturb the measurements of the wireless channel.To address this challenge,we analyze the CSI signals and find the typical signal frequencies generated by gestures,which are different from those generated by movements of other body parts.We then design a robust estimation algorithm,called LEVD,to remove the impact of environmental dynamics. Separate gesture movements from daily activities:Daily activities,such as walking and sitting down,also distort the wireless channel state information.To ensure that QGesture only responses to the channel distortion caused by specific gestures,we design simple gestures with unique characteristics as preambles to determine the start of the gesture.Our experimental results show that QGesture can efficiently recognize the preamble with an accuracy of 92.5%and a low False Positive Rate(FPR)of 3.2%. Accommodate arbitrary pushing angles:The phase changes of CSI measurements are determined by the changes in path length,which depends on the movement angle and the position of the hand with respect to the sender and receiver.When the hand moves along the line connecting the sender and receiver,the path length changes by two times of the movement distance.However,when the movement is in other directions,we may get smaller path length change for the same movement distance.To allow pushing along arbitrary angle,we need to perform the 2D tracking of the hand.To address this challenge,we propose to use multiple receivers to track path length changes of different paths at the same time.By doing this,we can triangulate the position of the hand and measure both the pushing angle and the movement distance.Our experimental results show that we can measure the movement angle with an accuracy of 15 degrees and movement distance with an accuracy of 3.7 cm. We implemented QGesture using COTS WiFi routers and laptops.Our experimental results show that QGes- ture can measure the gesture movement distance with an accuracy of 3 cm within a distance of 1 meters in normal indoor environments.OGesture can also reliably detect the hand movement direction with an accuracy of more than 95%in the one-dimensional case.Furthermore,it achieves an average absolute direction error of 15 degrees and an average accuracy of 3.7 cm in the measurement of movement distance in the two-dimensional case. 2 RELATED WORK We classify existing related gesture systems into two groups:RF-based recognition/tracking and non-RF-based recognition/tracking.Considering the way of collecting RF signal,we further classify RF-based into two cate- gories:RF-based recognition/tracking using COTS hardware and RF-based recognition/tracking using special- ized devices. RF-based Recognition/Tracking Using COTS Hardware:Most COTS hardware based on recognition and tracking systems uses the Received Signal Strength Indicator(RSSI)or CSI obtained from WiFi NICs to capture gesture signals [1,5,7,13,18,22,25,26].The WiKey scheme proposed to use CSI dynamics to recognize micro human activities such as keystrokes [6].The WiFinger scheme used CSI to recognize a set of eight gestures with an accuracy of 93%[26].The WiGest scheme used three wireless links to recognize a special set of gestures, where user hands blocked the signal and thus introduced significant RSSI changes,and achieved a recognition 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 • 39:3 variations without disturbing the small phase changes caused by hand movements. To address this challenge, we carefully analyze the phase offsets in different antenna pairs and design our phase correction algorithm so that phase changes of hand movements can be preserved. Hence, we can determine the movement direction with an accuracy of more than 95%. • Separate the channel state changes caused by the moving hands from the mixture of changes caused by other body parts: This is particularly important for a gesture recognition system to operate over a long distance, i.e., several meters, because such system captures both the gesture movements and the environmental dynamics. When the user performs the gesture, their torso and arms also move at the same time, which significantly perturb the measurements of the wireless channel. To address this challenge, we analyze the CSI signals and find the typical signal frequencies generated by gestures, which are different from those generated by movements of other body parts. We then design a robust estimation algorithm, called LEVD, to remove the impact of environmental dynamics. • Separate gesture movements from daily activities: Daily activities, such as walking and sitting down, also distort the wireless channel state information. To ensure that QGesture only responses to the channel distortion caused by specific gestures, we design simple gestures with unique characteristics as preambles to determine the start of the gesture. Our experimental results show that QGesture can efficiently recognize the preamble with an accuracy of 92.5% and a low False Positive Rate (FPR) of 3.2%. • Accommodate arbitrary pushing angles: The phase changes of CSI measurements are determined by the changes in path length, which depends on the movement angle and the position of the hand with respect to the sender and receiver. When the hand moves along the line connecting the sender and receiver, the path length changes by two times of the movement distance. However, when the movement is in other directions, we may get smaller path length change for the same movement distance. To allow pushing along arbitrary angle, we need to perform the 2D tracking of the hand. To address this challenge, we propose to use multiple receivers to track path length changes of different paths at the same time. By doing this, we can triangulate the position of the hand and measure both the pushing angle and the movement distance. Our experimental results show that we can measure the movement angle with an accuracy of 15 degrees and movement distance with an accuracy of 3.7 cm. We implemented QGesture using COTS WiFi routers and laptops. Our experimental results show that QGes￾ture can measure the gesture movement distance with an accuracy of 3 cm within a distance of 1 meters in normal indoor environments. QGesture can also reliably detect the hand movement direction with an accuracy of more than 95% in the one-dimensional case. Furthermore, it achieves an average absolute direction error of 15 degrees and an average accuracy of 3.7 cm in the measurement of movement distance in the two-dimensional case. 2 RELATED WORK We classify existing related gesture systems into two groups: RF-based recognition/tracking and non-RF-based recognition/tracking. Considering the way of collecting RF signal, we further classify RF-based into two cate￾gories: RF-based recognition/tracking using COTS hardware and RF-based recognition/tracking using special￾ized devices. RF-based Recognition/Tracking Using COTS Hardware: Most COTS hardware based on recognition and tracking systems uses the Received Signal Strength Indicator (RSSI) or CSI obtained from WiFi NICs to capture gesture signals [1, 5, 7, 13, 18, 22, 25, 26]. The WiKey scheme proposed to use CSI dynamics to recognize micro human activities such as keystrokes [6]. The WiFinger scheme used CSI to recognize a set of eight gestures with an accuracy of 93% [26]. The WiGest scheme used three wireless links to recognize a special set of gestures, where user hands blocked the signal and thus introduced significant RSSI changes, and achieved a recognition Proceedings of the ACM on Human-Computer Interaction, Vol. 1, No. 4, Article 39. Publication date: March 2018