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Fine-grained Handwriting Recognition Using Wi-Fi Signals in SIMO Scenario Based on WKNN
LI Xudong,JIANG Ting *,PAN Xu,DING Xue
Key Laboratory of Universal Wireless Communication, Beijing University of Posts and Telecommunications Beijing, China
*Correspondence author
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Funding: none
Opened online: 4 March 2019
Accepted by: none
Citation: LI Xudong,JIANG Ting,PAN Xu.Fine-grained Handwriting Recognition Using Wi-Fi Signals in SIMO Scenario Based on WKNN[OL]. [ 4 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4747345
 
 
Using wireless signals to implement new type of human-computer interaction is a hot topic in recent years. In this paper, we try to use the received signal strength indicators (RSSI) of multi-channel Wi-Fi frames to achieve fine-grained handwriting recognition. A New method of Handwriting Detection Algorithm whose average precision rate and recall rate of handwriting detection have been respectively achieved to 91.77% and 97.19% is proposed. We also designed a new kind of weighted k-nearest neighbors algorithm (WKNN) which is improved by multi-dimensional dynamic time warping (MD-DTW). It can achieve an average accuracy of 91% in the classification of 26 letters in experimental environment.
Keywords:Signal and signal processing; Handwriting Recognition; Wi-Fi; RSSI; Multi-dimensional dynamic time warping (MD-DTW); Weighted k-nearest neighbor classification (WKNN)
 
 
 

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