skip to main content
10.1145/2973750.2973764acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmobicomConference Proceedingsconference-collections
demonstration
Public Access

Device-free gesture tracking using acoustic signals

Published:03 October 2016Publication History

ABSTRACT

Device-free gesture tracking is an enabling HCI mechanism for small wearable devices because fingers are too big to control the GUI elements on such small screens, and it is also an important HCI mechanism for medium-to-large size mobile devices because it allows users to provide input without blocking screen view. In this paper, we propose LLAP, a device-free gesture tracking scheme that can be deployed on existing mobile devices as software, without any hardware modification. We use speakers and microphones that already exist on most mobile devices to perform device-free tracking of a hand/finger. The key idea is to use acoustic phase to get fine-grained movement direction and movement distance measurements. LLAP first extracts the sound signal reflected by the moving hand/finger after removing the background sound signals that are relatively consistent over time. LLAP then measures the phase changes of the sound signals caused by hand/finger movements and then converts the phase changes into the distance of the movement. We implemented and evaluated LLAP using commercial-off-the-shelf mobile phones. For 1-D hand movement and 2-D drawing in the air, LLAP has a tracking accuracy of 3.5 mm and 4.6 mm, respectively. Using gesture traces tracked by LLAP, we can recognize the characters and short words drawn in the air with an accuracy of 92.3% and 91.2%, respectively.

References

  1. Google project soli. https://www.google.com/atap/project-soli/.Google ScholarGoogle Scholar
  2. Teng Wei and Xinyu Zhang. mTrack: High-precision passive tracking using millimeter wave radios. In Proc. ACM MobiCom, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Chi Zhang, Josh Tabor, Jialiang Zhang, and Xinyu Zhang. Extending mobile interaction through near-field visible light sensing. In Proc. ACM MobiCom, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Leap Motion. https://www.leapmotion.com/.Google ScholarGoogle Scholar
  5. Rajalakshmi Nandakumar, Vikram Iyer, Desney Tan, and Shyamnath Gollakota. FingerIO: Using active sonar for fine-grained finger tracking. In Proc. ACM CHI, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A Rodrìguez Valiente, A Trinidad, JR García Berrocal, C Górriz, and R Ramírez Camacho. Extended high-frequency (9--20 kHz) audiometry reference thresholds in 645 healthy subjects. International journal of audiology, 53(8):531--545, 2014.Google ScholarGoogle Scholar
  7. Chunyi Peng, Guobin Shen, Yongguang Zhang, Yanlin Li, and Kun Tan. Beepbeep: a high accuracy acoustic ranging system using COTS mobile devices. In Proc. ACM SenSys, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Zengbin Zhang, David Chu, Xiaomeng Chen, and Thomas Moscibroda. Swordfight: Enabling a new class of phone-to-phone action games on commodity phones. In Proc. ACM MobiSys, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Sidhant Gupta, Daniel Morris, Shwetak Patel, and Desney Tan. Soundwave: using the doppler effect to sense gestures. In Proc. ACM CHI, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Zheng Sun, Aveek Purohit, Raja Bose, and Pei Zhang. Spartacus: spatially-aware interaction for mobile devices through energy-efficient audio sensing. In Proc. ACM MobiSys, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Md Tanvir Islam Aumi, Sidhant Gupta, Mayank Goel, Eric Larson, and Shwetak Patel. Doplink: Using the doppler effect for multi-device interaction. In Proc. ACM UbiComp, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Ke-Yu Chen, Daniel Ashbrook, Mayank Goel, Sung-Hyuck Lee, and Shwetak Patel. Airlink: sharing files between multiple devices using in-air gestures. In Proc. ACM UbiComp, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Sangki Yun, Yi-Chao Chen, and Lili Qiu. Turning a mobile device into a mouse in the air. In Proc. ACM MobiSys, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Nissanka B Priyantha, Anit Chakraborty, and Hari Balakrishnan. The cricket location-support system. In Proc. ACM MobiCom, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Jie Yang, Simon Sidhom, Gayathri Chandrasekaran, Tam Vu, Hongbo Liu, Nicolae Cecan, Yingying Chen, Marco Gruteser, and Richard P. Martin. Detecting driver phone use leveraging car speakers. In Proc. ACM MobiCom, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Yu-Chih Tung and Kang G Shin. Echotag: Accurate infrastructure-free indoor location tagging with smartphones. In Proc. ACM MobiCom, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Wenchao Huang, Yan Xiong, Xiang-Yang Li, Hao Lin, Xufei Mao, Panlong Yang, and Yunhao Liu. Shake and walk: Acoustic direction finding and fine-grained indoor localization using smartphones. In Proc. IEEE INFOCOM, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  18. Rajalakshmi Nandakumar, Shyamnath Gollakota, and Nathaniel Watson. Contactless sleep apnea detection on smartphones. In Proc. ACM MobiSys, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Junjue Wang, Kaichen Zhao, Xinyu Zhang, and Chunyi Peng. Ubiquitous keyboard for small mobile devices: harnessing multipath fading for fine-grained keystroke localization. In Proc. ACM MobiSys, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Tong Zhu, Qiang Ma, Shanfeng Zhang, and Yunhao Liu. Context-free attacks using keyboard acoustic emanations. In Proc. ACM CCS, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Jian Liu, Yan Wang, Gorkem Kar, Yingying Chen, Jie Yang, and Marco Gruteser. Snooping keystrokes with mm-level audio ranging on a single phone. In Proc. ACM MobiCom, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Maotian Zhang, Panlong Yang, Chang Tian, Lei Shi, Shaojie Tang, and Fu Xiao. Soundwrite: Text input on surfaces through mobile acoustic sensing. In Proc. ACM SmartObjects, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, and Sanglu Lu. Understanding and modeling of WiFi signal based human activity recognition. In Proc. ACM MobiCom, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Kamran Ali, Alex X. Liu, Wei Wang, and Muhammad Shahzad. Keystroke recognition using WiFi signals. In Proc. ACM MobiCom, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Qifan Pu, Sidhant Gupta, Shyamnath Gollakota, and Shwetak Patel. Whole-home gesture recognition using wireless signals. In Proc. ACM MobiCom, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Fadel Adib, Zachary Kabelac, and Dina Katabi. Multi-person motion tracking via RF body reflections. In Proc. Usenix NSDI, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Bryce Kellogg, Vamsi Talla, and Shyamnath Gollakota. Bringing gesture recognition to all devices. In Proc. Usenix NSDI, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Yan Wang, Jian Liu, Yingying Chen, Marco Gruteser, Jie Yang, and Hongbo Liu. E-eyes: In-home device-free activity identification using fine-grained WiFi signatures. In Proc. ACM MobiCom, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Heba Abdelnasser, Moustafa Youssef, and Khaled A Harras. WiGest: A ubiquitous WiFi-based gesture recognition system. In Proc. IEEE INFOCOM, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  30. Pedro Melgarejo, Xinyu Zhang, Parameswaran Ramanathan, and David Chu. Leveraging directional antenna capabilities for fine-grained gesture recognition. In Proc. ACM UbiComp, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Li Sun, Souvik Sen, Dimitrios Koutsonikolas, and Kyu-Han Kim. WiDraw: Enabling hands-free drawing in the air on commodity wifi devices. In Proc. ACM MobiCom, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Jue Wang, Deepak Vasisht, and Dina Katabi. RF-IDraw: virtual touch screen in the air using RF signals. In Proc. ACM SIGCOMM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Microsoft Kinect. http://www.microsoft.com/en-us/kinectforwindows/.Google ScholarGoogle Scholar
  34. Robert Xiao, Chris Harrison, Karl DD Willis, Ivan Poupyrev, and Scott E Hudson. Lumitrack: low cost, high precision, high speed tracking with projected m-sequences. In Proc. ACM UIST, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Jie Song, Gábor Sörös, Fabrizio Pece, Sean Ryan Fanello, Shahram Izadi, Cem Keskin, and Otmar Hilliges. In-air gestures around unmodified mobile devices. In Proc. ACM UIST, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Leon Cohen. Time-frequency analysis. Prentice hall, 1995.Google ScholarGoogle Scholar
  37. David Tse and Pramod Viswanath. Fundamentals of wireless communication. Cambridge university press, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Norden E Huang, Zheng Shen, Steven R Long, Manli C Wu, Hsing H Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, and Henry H Liu. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, volume 454, pages 903--995. The Royal Society, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  39. AK Agnihotri, B Purwar, N Jeebun, and S Agnihotri. Determination of sex by hand dimensions. The Internet Journal of Forensic Science, 1(2):12--24, 2006.Google ScholarGoogle Scholar
  40. MyScript. http://myscript.com/.Google ScholarGoogle Scholar
  41. Knowles Electronics. SPH0641LU4H-1: Digital zero-height SiSonicmicophone with multi-mode and ultrasonic support, 2014.Google ScholarGoogle Scholar

Index Terms

  1. Device-free gesture tracking using acoustic signals

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      MobiCom '16: Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking
      October 2016
      532 pages
      ISBN:9781450342261
      DOI:10.1145/2973750

      Copyright © 2016 ACM

      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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 3 October 2016

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • demonstration

      Acceptance Rates

      MobiCom '16 Paper Acceptance Rate31of226submissions,14%Overall Acceptance Rate440of2,972submissions,15%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    ePub

    View this article in ePub.

    View ePub