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Poster: Enabling Hands-free Drawing on WiFi Devices

Published: 11 September 2015 Publication History

Abstract

We present the high-level design of WiDraw, the first 3D hand motion tracking system using COTS WiFi cards, proposed in [3]. WiDraw can track hand gestures without a priori learning or requiring the user to hold any hardware. It can be implemented on existing mobile devices using only a software patch. WiDraw exploits the Angle-of-Arrival values of incomingWiFi signals at aWiFi device and enables hand trajectory tracking in both LOS and NLOS scenarios. WiDraw's high tracking accuracy allows the users to send arbitrary input to their WiFi devices, opening up a whole new class of applications in human-computer interactions.

References

[1]
Q. Pu, S. Gupta, S. Gollakota, and S. Patel. Whole-home gesture recognition using wireless signals. In Proceedings of ACM Mobicom, 2013.
[2]
J. Wang, D. Vasisht, and D. Katabi. RF-IDraw: Virtual Touch Screen in the Air Using RF Signals. In Proceedings of ACM SIGCOMM, 2014.
[3]
L. Sun, S. Sen, and D. Koutsonikolas. WiDraw: Enabling Hands-free Drawing in the Air on Commodity WiFi Devices. In Proceedings of the ACM MobiCom, 2015.

Cited By

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  • (2017)A fall detection algorithm based on channel state information2017 International Smart Cities Conference (ISC2)10.1109/ISC2.2017.8090862(1-4)Online publication date: Sep-2017

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Published In

cover image ACM Conferences
S3 '15: Proceedings of the 2015 Workshop on Wireless of the Students, by the Students, & for the Students
September 2015
56 pages
ISBN:9781450337014
DOI:10.1145/2801694
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 September 2015

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Author Tags

  1. angle-of-arrival
  2. gesture recognition
  3. motion tracking
  4. wireless

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MobiCom'15
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S3 '15 Paper Acceptance Rate 10 of 25 submissions, 40%;
Overall Acceptance Rate 65 of 93 submissions, 70%

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Cited By

View all
  • (2017)A fall detection algorithm based on channel state information2017 International Smart Cities Conference (ISC2)10.1109/ISC2.2017.8090862(1-4)Online publication date: Sep-2017

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