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Wisture: Touch-Less Hand Gesture Classification in Unmodified Smartphones Using Wi-Fi Signals
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
Univ Georgia, Dept Comp Sci, Athens, GA 30602 USA..
2019 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 19, no 1, p. 257-267Article in journal (Refereed) Published
Abstract [en]

This paper introduces Wisture, a new online machine learning solution for recognizing touch-less hand gestures on a smartphone (mobile device). Wisture relies on the standard Wi-Fi received signal strength measurements, long short-term memory recurrent neural network (RNN) learning method, thresholding filters, and a traffic induction approach. Unlike other Wi-Fi-based gesture recognition methods, the proposed method does not require a modification of the device hardware or the operating system and performs the gesture recognition without interfering with the normal operation of other smartphone applications. We discuss the characteristics of Wisture and conduct extensive experiments to compare the performance of the RNN learning method against the state-of the-art machine learning solutions regarding both accuracy and efficiency. The experiments include a set of different scenarios with a change in spatial setup and network traffic between the smartphone and Wi-Fi access points. The results show that Wisture achieves an online gesture recognition accuracy of up to 93% (average 78%) in detecting and classifying three gestures.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 19, no 1, p. 257-267
Keywords [en]
Wi-Fi, radio signal strength, gesture recognition, mobile phones, machine learning, traffic induction
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-240697DOI: 10.1109/JSEN.2018.2876448ISI: 000452623200029Scopus ID: 2-s2.0-85055056529OAI: oai:DiVA.org:kth-240697DiVA, id: diva2:1277720
Note

QC 20190111

Available from: 2019-01-11 Created: 2019-01-11 Last updated: 2019-01-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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