Word-Fi: Accurate Handwrite System Empowered by Wireless Backscattering and Machine Learning

Word-Fi is a handwriting input system, driven by wireless backscattering technology and machine learning methods. It could effectively mitigate the surrounding noise and extract the weak signals incurred by tiny writing gestures accurately. Leveraging our customized wireless backscattering system, Word-Fi could be noise tolerant across relatively complex environments, especially when multiple persons are presented around, which significantly differs from status quo wireless sensing systems that suffer from multi-user presentation. For weak signal extraction, Word- Fi incorporates an efficient feature selection scheme for classification and improves the classifier by fully exploiting the physical layer information. After using the word suggestion module, it could recognize writing words with fairly high accuracy (above 90 percent) across different volunteers (7-10).

[1]  Muhammad Shahzad,et al.  Position and Orientation Agnostic Gesture Recognition Using WiFi , 2017, MobiSys.

[2]  Fadel Adib,et al.  See through walls with WiFi! , 2013, SIGCOMM.

[3]  Yu Gu,et al.  Activity Recognition via Channel Response: From Theoretical Analysis to Real-World Experiments , 2017, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).

[4]  Sangki Yun,et al.  Strata: Fine-Grained Acoustic-based Device-Free Tracking , 2017, MobiSys.

[5]  Parameswaran Ramanathan,et al.  Leveraging directional antenna capabilities for fine-grained gesture recognition , 2014, UbiComp.

[6]  Joshua R. Smith,et al.  Wi-fi backscatter , 2014, SIGCOMM 2015.

[7]  Xia Zhou,et al.  Human Sensing Using Visible Light Communication , 2015, MobiCom.

[8]  Joshua R. Smith,et al.  Battery-Free Cellphone , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[9]  Yubo Yan,et al.  Motion-Fi: Recognizing and Counting Repetitive Motions with Passive Wireless Backscattering , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[10]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[11]  David Wetherall,et al.  Ambient backscatter: wireless communication out of thin air , 2013, SIGCOMM.

[12]  Klara Nahrstedt,et al.  WritingHacker: audio based eavesdropping of handwriting via mobile devices , 2016, UbiComp.

[13]  Shu Wang,et al.  Acoustic Eavesdropping through Wireless Vibrometry , 2015, MobiCom.