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Open AccessProceedings ArticleDOI

RF-IDraw: virtual touch screen in the air using RF signals

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TLDR
This paper shows that one can provide a dramatic increase in trajectory tracing accuracy, even with a small number of antennas, by exploiting an intrinsic tradeoff between improving the resolution and resolving ambiguity in the location of the RF source.
Abstract
Prior work in RF-based positioning has mainly focused on discovering the absolute location of an RF source, where state-of-the-art systems can achieve an accuracy on the order of tens of centimeters using a large number of antennas. However, many applications in gaming and gesture based interface see more benefits in knowing the detailed shape of a motion. Such trajectory tracing requires a resolution several fold higher than what existing RF-based positioning systems can offer. This paper shows that one can provide a dramatic increase in trajectory tracing accuracy, even with a small number of antennas. The key enabler for our design is a multi-resolution positioning technique that exploits an intrinsic tradeoff between improving the resolution and resolving ambiguity in the location of the RF source. The unique property of this design is its ability to precisely reconstruct the minute details in the trajectory shape, even when the absolute position might have an offset. We built a prototype of our design with commercial off-the-shelf RFID readers and tags and used it to enable a virtual touch screen, which allows a user to interact with a desired computing device by gesturing or writing her commands in the air, where each letter is only a few centimeters wide.

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TL;DR: SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems.
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Capturing the human figure through a wall

TL;DR: RF-Capture tracks the 3D positions of a person's limbs and body parts even when the person is fully occluded from its sensor, and does so without placing any markers on the subject's body.
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Device-free gesture tracking using acoustic signals

TL;DR: This paper proposes LLAP, a device-free gesture tracking scheme that can be deployed on existing mobile devices as software, without any hardware modification, and implemented and evaluated LLAP using commercial-off-the-shelf mobile phones.
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ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization

TL;DR: ToneTrack as discussed by the authors is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth.
References
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Journal ArticleDOI

Online and off-line handwriting recognition: a comprehensive survey

TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Proceedings ArticleDOI

Zee: zero-effort crowdsourcing for indoor localization

TL;DR: Zee is presented -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users.
Proceedings ArticleDOI

Whole-home gesture recognition using wireless signals

TL;DR: WiSee is presented, a novel gesture recognition system that leverages wireless signals (e.g., Wi-Fi) to enable whole-home sensing and recognition of human gestures and achieves this goal without requiring instrumentation of the human body with sensing devices.
Proceedings ArticleDOI

Indoor localization without the pain

TL;DR: Despite the absence of any explicit pre-deployment calibration, EZ yields a median localization error of 2m and 7m in a small building and a large building, which is only somewhat worse than the 0.7m and 4m yielded by the best-performing but calibration-intensive Horus scheme from prior work.
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