39 QGesture:Quantifying Gesture Distance and Direction with WiFi Signals NAN YU,State Key Laboratory for Novel Software Technology,Nanjing University,China WEI WANG,State Key Laboratory for Novel Software Technology,Nanjing University,China ALEX X.LIU,Deptartment of Computer Science of Engineering.Michigan State University,USA LINGTAO KONG,State Key Laboratory for Novel Software Technology,Nanjing University,China Many HCI applications,such as volume adjustment in a gaming system,require quantitative gesture measurement for metrics such as movement distance and direction.In this paper,we propose QGesture,a gesture recognition system that uses CSI values provided by COTS WiFi devices to measure the movement distance and direction of human hands.To achieve high accuracy in measurements,we first use phase correction algorithm to remove the phase noise in CSI measurements.We then propose a robust estimation algorithm,called LEVD,to estimate and remove the impact of environmental dynamics. To separate gesture movements from daily activities,we design simple gestures with unique characteristics as preambles to determine the start of the gesture.Our experimental results show that QGesture achieves an average accuracy of 3 cm in the measurement of movement distance and more than 95%accuracy in the movement direction detection 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. CCS Concepts:Human-centered computing-Ubiquitous and mobile computing systems and tools; Additional Key Words and Phrases:Gesture Recognition,WiFi Signals,Wireless Sensing ACM Reference Format: Nan Yu,Wei Wang.Alex X.Liu,and Lingtao Kong.2018.QGesture:Quantifying Gesture Distance and Direction with WiFi Signals.Proc.ACM Hum.-Comput.Interact.1,4,Article 39(March 2018),22 pages.https://doi.org/0000001. 0000001 1 INTRODUCTION Recently a number of interesting WiFi-based gesture recognition schemes have been proposed [1,8,19,24,29]. As human bodies are mostly made of water,they reflect WiFi signals and introduce distortions in the received signal when they move.Different gestures cause different types of distortions in WiFi signals.Thus,by analyzing the changes in WiFi signals,we can recognize the corresponding gesture.WiFi-based gesture recognition has many advantages over traditional approaches that use cameras [4]or wearable sensors [10,28,36].For exam- ple,WiFi-based gesture recognition requires neither lighting nor carrying any devices.It also provides better coverage as WiFi signals can penetrate through walls. Authors'addresses:Nan Yu,State Key Laboratory for Novel Software Technology,Nanjing University,State Key Laboratory for Novel Software Technology,Nanjing.Jiangsu,China;Wei Wang.State Key Laboratory for Novel Software Technology,Nanjing University,State Key Laboratory for Novel Software Technology,Nanjing.Jiangsu,China;Alex X.Liu,Deptartment of Computer Science of Engineering. Michigan State University,Computer Science and Engineering.USA:Lingtao Kong.State Key Laboratory for Novel Software Technology, Nanjing University,State Key Laboratory for Novel Software Technology,Nanjing.Jiangsu,China. ACM acknowledges that this contribution was authored or co-authored by an employee,contractor,or affiliate of the United States govern ment.As such,the United States government retains a nonexclusive,royalty-free right to publish or reproduce this article,or to allow others to do so,for government purposes only. 2018 Association for Computing Machinery. 2573-0142/2018/3-ART39$15.00 https:/doi.org/0000001.0000001 Proceedings of the ACM on Human-Computer Interaction,Vol.1,No.4,Article 39.Publication date:March 2018.39 QGesture: Quantifying Gesture Distance and Direction with WiFi Signals NAN YU, State Key Laboratory for Novel Software Technology, Nanjing University, China WEI WANG, State Key Laboratory for Novel Software Technology, Nanjing University, China ALEX X. LIU, Deptartment of Computer Science of Engineering, Michigan State University, USA LINGTAO KONG, State Key Laboratory for Novel Software Technology, Nanjing University, China Many HCI applications, such as volume adjustment in a gaming system, require quantitative gesture measurement for metrics such as movement distance and direction. In this paper, we propose QGesture, a gesture recognition system that uses CSI values provided by COTS WiFi devices to measure the movement distance and direction of human hands. To achieve high accuracy in measurements, we first use phase correction algorithm to remove the phase noise in CSI measurements. We then propose a robust estimation algorithm, called LEVD, to estimate and remove the impact of environmental dynamics. To separate gesture movements from daily activities, we design simple gestures with unique characteristics as preambles to determine the start of the gesture. Our experimental results show that QGesture achieves an average accuracy of 3 cm in the measurement of movement distance and more than 95% accuracy in the movement direction detection 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. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; Additional Key Words and Phrases: Gesture Recognition, WiFi Signals, Wireless Sensing ACM Reference Format: Nan Yu, Wei Wang, Alex X. Liu, and Lingtao Kong. 2018. QGesture: Quantifying Gesture Distance and Direction with WiFi Signals. Proc. ACM Hum.-Comput. Interact. 1, 4, Article 39 (March 2018), 22 pages. https://doi.org/0000001. 0000001 1 INTRODUCTION Recently a number of interesting WiFi-based gesture recognition schemes have been proposed [1, 8, 19, 24, 29]. As human bodies are mostly made of water, they reflect WiFi signals and introduce distortions in the received signal when they move. Different gestures cause different types of distortions in WiFi signals. Thus, by analyzing the changes in WiFi signals, we can recognize the corresponding gesture. WiFi-based gesture recognition has many advantages over traditional approaches that use cameras [4] or wearable sensors [10, 28, 36]. For exam￾ple, WiFi-based gesture recognition requires neither lighting nor carrying any devices. It also provides better coverage as WiFi signals can penetrate through walls. Authors’ addresses: Nan Yu, State Key Laboratory for Novel Software Technology, Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, Jiangsu, China; Wei Wang, State Key Laboratory for Novel Software Technology, Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, Jiangsu, China; Alex X. Liu, Deptartment of Computer Science of Engineering, Michigan State University, Computer Science and Engineering, USA; Lingtao Kong, State Key Laboratory for Novel Software Technology, Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, Jiangsu, China. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor, or affiliate of the United States govern￾ment. As such, the United States government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for government purposes only. © 2018 Association for Computing Machinery. 2573-0142/2018/3-ART39 $15.00 https://doi.org/0000001.0000001 Proceedings of the ACM on Human-Computer Interaction, Vol. 1, No. 4, Article 39. Publication date: March 2018