39:2·N.Yu et al One of the most important applications of WiFi-based gesture recognition is to interact with smart home devices.Existing home appliances use physical interfaces,such as knobs and levers,to provide quantitative inputs,including volume adjustment for TVs and brightness adjustment for lights.These physical inputs allow the user to fine-tune the input value based on immediate feedback.It is difficult to emulate these physical inputs using popular voice-based interactions provided by Amazon Echo or Google Home.However,WiFi-based gesture control can enable such fine-grained quantitative control.For example,the user can push his hand forward to increase the volume of the TV set,where the magnitude of volume increase is proportional to the distance of pushing.To enable this,we need not only to recognize different predefined gestures,but also to quantify gesture movement distance in a granularity of a few centimeters so that the system can adjust the volume according to the distance that the user pushes his hand,while providing audio feedback on the current volume setting along the pushing process.In this way,the user can quantitatively adjust the volume to the desired value using a single action rather than repeating the gesture to increase or decrease the volume by a small amount at each time. The task of using Radio Frequency(RF)signal obtained from commercial hardware to measure the ges- ture movement distance and direction is difficult.Prior systems that use WiFi measurements from commer- cial devices often require whole-body movements,such as walking,to track movement speeds and directions [3,24,33,37].With the coarse measurement from commercial WiFi devices,existing schemes cannot trace hand/finger movements,which introduces weaker WiFi signal distortions than whole-body movements,in a fine-granularity.(delete?)They can only recognize hand/finger movements by matching them to predefined ges- ture patterns [1,18].Using customized ASIC chips based on the 60 GHz radar technology,Google's recent Soli system can quantify micro gestures so that those gestures can serve as human input for small wearable devices (such as smart watches)whose touch screens are too small for human to conveniently input [19].However,due to the fast decay of 60 GHz signal in the air,60 GHz system requires the gesture to be performed within tens of centimeters [31].The limited operational range makes them unsuitable to serve as remote control interfaces for home appliances. In this paper,we propose QGesture,a Quantifying Gesture distance and direction system,which uses Commercial- Off-The-Shelf(COTS)WiFi devices to measure the movement distance and direction of human hands.Figure 1 shows the basic system structure of OGesture.When the user pushes towards the target device,the device collects Channel State Information(CSI),which is perturbed by the WiFi signal reflected by the moving hand The signal reflected by the hand appears as a dynamic vector component in the CSI values,which causes the complex-valued CSI measurement to rotate.The distance of movement can be calculated by the phase change of complex-valued CSI measurement and the direction of movement can be determined by the rotation direction. Therefore,the user can push forward to increase the volume while pulling away to reduce the volume and the amount of increase/decrease is determined by the movement distance.As the perturbation of WiFi signals can be captured at a long distance,QGesture can work at a distance as far as 2 meters.QGesture is the first step to- wards quantitative remote control for home appliances.It shows the feasibility of fine-grained distance/direction measurement of hand movement over a few meters using COTS WiFi devices.Note that currently only a limited modules of WiFi network cards can provide CSI measurements [12]and CSI is not available on smartphones. We envision that more commercial WiFi devices would open their CSI information so that our approach can be deployed on smartphones in the near future. There are four key challenges that need to be addressed in designing QGesture. Reconstruct the phase of CSI measurements:The phase of CSI measurements is important for determining the movement directions [29].However,due to hardware imperfections in COTS WiFi Network Interface Cards (NICs),there are Carrier Frequency Offsets(CFO)and Sampling Frequency Offsets(SFO)between the transmitter and the receiver [17,34].Both the CFO and SFO introduce high variations in the phase of CSI and these variations are sensitive to temperature and hardware conditions.Therefore,it is difficult to predict and remove such phase Proceedings of the ACM on Human-Computer Interaction,Vol.1,No.4,Article 39.Publication date:March 2018.39:2 • N. Yu et al. One of the most important applications of WiFi-based gesture recognition is to interact with smart home devices. Existing home appliances use physical interfaces, such as knobs and levers, to provide quantitative inputs, including volume adjustment for TVs and brightness adjustment for lights. These physical inputs allow the user to fine-tune the input value based on immediate feedback. It is difficult to emulate these physical inputs using popular voice-based interactions provided by Amazon Echo or Google Home. However, WiFi-based gesture control can enable such fine-grained quantitative control. For example, the user can push his hand forward to increase the volume of the TV set, where the magnitude of volume increase is proportional to the distance of pushing. To enable this, we need not only to recognize different predefined gestures, but also to quantify gesture movement distance in a granularity of a few centimeters so that the system can adjust the volume according to the distance that the user pushes his hand, while providing audio feedback on the current volume setting along the pushing process. In this way, the user can quantitatively adjust the volume to the desired value using a single action rather than repeating the gesture to increase or decrease the volume by a small amount at each time. The task of using Radio Frequency (RF) signal obtained from commercial hardware to measure the ges￾ture movement distance and direction is difficult. Prior systems that use WiFi measurements from commer￾cial devices often require whole-body movements, such as walking, to track movement speeds and directions [3, 24, 33, 37]. With the coarse measurement from commercial WiFi devices, existing schemes cannot trace hand/finger movements, which introduces weaker WiFi signal distortions than whole-body movements, in a fine-granularity.(delete?) They can only recognize hand/finger movements by matching them to predefined ges￾ture patterns [1, 18]. Using customized ASIC chips based on the 60 GHz radar technology, Google’s recent Soli system can quantify micro gestures so that those gestures can serve as human input for small wearable devices (such as smart watches) whose touch screens are too small for human to conveniently input [19]. However, due to the fast decay of 60 GHz signal in the air, 60 GHz system requires the gesture to be performed within tens of centimeters [31]. The limited operational range makes them unsuitable to serve as remote control interfaces for home appliances. In this paper, we propose QGesture, a Quantifying Gesture distance and direction system, which uses Commercial￾Off-The-Shelf (COTS) WiFi devices to measure the movement distance and direction of human hands. Figure 1 shows the basic system structure of QGesture. When the user pushes towards the target device, the device collects Channel State Information (CSI), which is perturbed by the WiFi signal reflected by the moving hand. The signal reflected by the hand appears as a dynamic vector component in the CSI values, which causes the complex-valued CSI measurement to rotate. The distance of movement can be calculated by the phase change of complex-valued CSI measurement and the direction of movement can be determined by the rotation direction. Therefore, the user can push forward to increase the volume while pulling away to reduce the volume and the amount of increase/decrease is determined by the movement distance. As the perturbation of WiFi signals can be captured at a long distance, QGesture can work at a distance as far as 2 meters. QGesture is the first step to￾wards quantitative remote control for home appliances. It shows the feasibility of fine-grained distance/direction measurement of hand movement over a few meters using COTS WiFi devices. Note that currently only a limited modules of WiFi network cards can provide CSI measurements [12] and CSI is not available on smartphones. We envision that more commercial WiFi devices would open their CSI information so that our approach can be deployed on smartphones in the near future. There are four key challenges that need to be addressed in designing QGesture. • Reconstruct the phase of CSI measurements: The phase of CSI measurements is important for determining the movement directions [29]. However, due to hardware imperfections in COTS WiFi Network Interface Cards (NICs), there are Carrier Frequency Offsets (CFO) and Sampling Frequency Offsets (SFO) between the transmitter and the receiver [17, 34]. Both the CFO and SFO introduce high variations in the phase of CSI and these variations are sensitive to temperature and hardware conditions. Therefore, it is difficult to predict and remove such phase Proceedings of the ACM on Human-Computer Interaction, Vol. 1, No. 4, Article 39. Publication date: March 2018