WiReader: Adaptive Air Handwriting Recognition Based on Commercial WiFi Signal

被引:35
作者
Guo, Zhengxin [1 ,2 ]
Xiao, Fu [1 ,2 ]
Sheng, Biyun [1 ,2 ]
Fei, Huan [1 ,2 ]
Yu, Shui [3 ]
机构
[1] Nanjing Univ Posts & Telecommun, Sch Comp, Nanjing 210023, Peoples R China
[2] Nanjing Univ Posts & Telecommun, Jiangsu High Technol Res Key Lab Wireless Sensor, Nanjing 210023, Peoples R China
[3] Univ Technol Sydney, Sch Comp Sci, Sydney, NSW 2007, Australia
基金
中国国家自然科学基金;
关键词
Handwriting recognition; Wireless communication; Wireless sensor networks; Sensors; Human computer interaction; Feature extraction; Wireless fidelity; Channel state information (CSI); CSI-Ratio; handwriting recognition; wireless sensing;
D O I
10.1109/JIOT.2020.2997053
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, with the rapid development of the Internet-of-Things (IoT) technologies, many intelligent sensing applications have emerged, which realize contactless sensing and human-computer interaction (HCI). Handwriting recognition is the communication link between the human and computer. Previous handwriting recognition applications are usually founded on images and sensors, which require significant device overhead and are device dependent. Recently, the revolution of the wireless signal sensing technology has laid the foundation for the intelligent handwriting recognition technology without devices. In this article, we propose WiReader, an adaptive air handwriting recognition system based on wireless signals. WiReader utilizes ubiquitous commercial WiFi devices to process the collected channel state information (CSI), segments the data in combination with activity factors, and then transforms the original signal using the CSI-Ratio model. In order to address the problem of feature extraction caused by handwriting, we utilize the cumulative principal components and multilayer wavelet transform for the transformed signal. Finally, the energy feature matrix is generated and combines with long short-term memory (LSTM) to realize the recognition of different handwriting actions. Extensive real-world experiments show that WiReader achieves an average recognition accuracy of 90.64% leading other applications in three scenarios and has strong robustness to user location, user diversity, and different scenarios.
引用
收藏
页码:10483 / 10494
页数:12
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