QGesture: Quantifying gesture distance and direction with WiFi signals

被引：37

作者：

Yu, Nan ^{[1
]}

Wang, Wei ^{[1
]}

Liu, Alex X. ^{[2
]}

Kong, Lingtao ^{[1
]}

机构：

[1] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China

[2] Deptartment of Computer Science of Engineering, Michigan State University, Computer Science and Engineering, United States

来源：

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies | 2018年 / 2卷 / 01期

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

Phase noise - Wireless local area networks (WLAN) - Machinery;

D O I：

10.1145/3191783

中图分类号：

O441.2 [磁学]; TM12 [];

学科分类号：

摘要：

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. 2018 Association for Computing Machinery.

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