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QGesture: Quantifying Gesture Distance and Direction with WiFi Signals

Published: 26 March 2018 Publication History

Abstract

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.

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    cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 2, Issue 1
    March 2018
    1370 pages
    EISSN:2474-9567
    DOI:10.1145/3200905
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 26 March 2018
    Accepted: 01 January 2018
    Revised: 01 November 2017
    Received: 01 May 2017
    Published in IMWUT Volume 2, Issue 1

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    Author Tags

    1. Gesture Recognition
    2. WiFi Signals
    3. Wireless Sensing

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    • (2025)WiPhase: A Human Activity Recognition Approach by Fusing of Reconstructed WiFi CSI Phase FeaturesIEEE Transactions on Mobile Computing10.1109/TMC.2024.346167224:1(394-406)Online publication date: Jan-2025
    • (2025)Device-Free Human Activity Recognition: A Systematic Literature ReviewIEEE Open Journal of Instrumentation and Measurement10.1109/OJIM.2024.35028854(1-34)Online publication date: 2025
    • (2024)Multi-relational graph attention network for social relationship inference from human mobility dataProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/256(2315-2323)Online publication date: 3-Aug-2024
    • (2024)GrainSense: A Wireless Grain Moisture Sensing System Based on Wi-Fi SignalsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785898:3(1-25)Online publication date: 9-Sep-2024
    • (2024)WiFi-CSI Difference ParadigmProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596088:2(1-29)Online publication date: 15-May-2024
    • (2024)UniFiProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314297:4(1-29)Online publication date: 12-Jan-2024
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    • (2024)A Deep Learning Based Lightweight Human Activity Recognition System Using Reconstructed WiFi CSIIEEE Transactions on Human-Machine Systems10.1109/THMS.2023.334869454:1(68-78)Online publication date: Feb-2024
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