research-article

FingerDraw: Sub-wavelength Level Finger Motion Tracking with WiFi Signals

Authors:

Daqing ZhangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 1

Article No.: 31, Pages 1 - 27

https://doi.org/10.1145/3380981

Published: 18 March 2020 Publication History

Abstract

This paper explores the possibility of tracking finger drawings in the air leveraging WiFi signals from commodity devices. Prior solutions typically require user to hold a wireless transmitter, or need proprietary wireless hardware. They can only recognize a small set of pre-defined hand gestures. This paper introduces FingerDraw, the first sub-wavelength level finger motion tracking system using commodity WiFi devices, without attaching any sensor to finger. FingerDraw can reconstruct finger drawing trajectory such as digits, alphabets, and symbols with the setting of one WiFi transmitter and two WiFi receivers. It uses a two-antenna receiver to sense the sub-wavelength scale displacement of finger motion in each direction. The theoretical underpinning of FingerDraw is our proposed CSI-quotient model, which uses the channel quotient between two antennas of the receiver to cancel out the noise in CSI amplitude and the random offsets in CSI phase, and quantifies the correlation between CSI value dynamics and object displacement. This channel quotient is sensitive to and enables us to detect small changes in In-phase and Quadrature parts of channel state information due to finger movement. Our experimental results show that the overall median tracking accuracy is 1.27 cm, and the recognition of drawing ten digits in the air achieves an average accuracy of over 93.0%.

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Cited By

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Li YWu DChen JShi WWang LSu LLi WZhang D(2025)SigCan: Toward Reliable ToF Estimation Leveraging Multipath Signal Cancellation on Commodity WiFi DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2024.349133724:3(1895-1912)Online publication date: Mar-2025
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Index Terms

FingerDraw: Sub-wavelength Level Finger Motion Tracking with WiFi Signals
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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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 4, Issue 1

March 2020

1006 pages

EISSN:2474-9567

DOI:10.1145/3388993

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Copyright © 2020 ACM.

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Published: 18 March 2020

Published in IMWUT Volume 4, Issue 1

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EU CHIST-ERA RadioSense Project
National Key Research and Development Plan
Peking University Information Technology Institute (Tianjin Binhai)
Australian Research Council (ARC) Discovery Project

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https://dl.acm.org/doi/10.1145/3711900
Li YWu DChen JShi WWang LSu LLi WZhang D(2025)SigCan: Toward Reliable ToF Estimation Leveraging Multipath Signal Cancellation on Commodity WiFi DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2024.349133724:3(1895-1912)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3491337
Ghosian Moghaddam MAsghar Nazari Shirehjini AShirmohammadi S(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
https://doi.org/10.1109/OJIM.2024.3502885
Wang ZZhang JZhang HXu MGuo J(2025)Passive Human Tracking With WiFi Point CloudsIEEE Internet of Things Journal10.1109/JIOT.2024.348719312:5(5528-5543)Online publication date: 1-Mar-2025
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Li WGao RXiong JZhou JWang LMao XYi EZhang D(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
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