WiDraw

Li Sun, Souvik Sen, Dimitrios Koutsonikolas, Kyu-Han Kim
<span title="">2015</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6ic3uq2bh5ezhkpheq3qkakozy" style="color: black;">Proceedings of the 21st Annual International Conference on Mobile Computing and Networking - MobiCom &#39;15</a> </i> &nbsp;
This paper demonstrates that it is possible to leverage WiFi signals from commodity mobile devices to enable hands-free drawing in the air. While prior solutions require the user to hold a wireless transmitter, or require custom wireless hardware, or can only determine a pre-defined set of hand gestures, this paper introduces WiDraw, the first hand motion tracking system using commodity WiFi cards, and without any user wearables. WiDraw harnesses the Angle-of-Arrival values of incoming wireless
more &raquo; ... signals at the mobile device to track the user's hand trajectory. We utilize the intuition that whenever the user's hand occludes a signal coming from a certain direction, the signal strength of the angle representing the same direction will experience a drop. Our software prototype using commodity wireless cards can track the user's hand with a median error lower than 5 cm. We use WiDraw to implement an in-air handwriting application that allows the user to draw letters, words, and sentences, and achieves a mean word recognition accuracy of 91%.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2789168.2790129">doi:10.1145/2789168.2790129</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/mobicom/SunSKK15.html">dblp:conf/mobicom/SunSKK15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/quwuvjcmozdo7gx5iqzkqfikgm">fatcat:quwuvjcmozdo7gx5iqzkqfikgm</a> </span>
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