Handwriting Tracking using 60 GHz mmWave Radar

Human-computer interaction is a vital component in today’s world and there is a constant quest for automated and user-friendly techniques for interaction. Handwriting is one of the most general and natural ways of interaction for humans. While handwriting recognition is a well-studied problem, its counterpart handwriting tracking is still being investigated. Most of the existing handwriting tracking systems either require sensors attached to the hand or involve specially designed hardware. In this work, we propose a handwriting tracking system that reuses a commodity 60GHz Wi-Fi radio as a radar. The moving target is localized at each time instance and a trajectory is constructed by connecting those location estimates. While the digital beamforming technique and the pulsed radar are used to recover the spatial and range information, Doppler velocity is used to isolate the moving target from the static objects. Further, subsample peak interpolation and smoothing techniques enhance the overall performance of the proposed system. Extensive experiments demonstrate an average tracking error of 2.5% of the distance from the device and validate the robustness of the system to different environments and experimental conditions.

[1]  Julius O. Smith,et al.  PARSHL: An Analysis/Synthesis Program for Non-Harmonic Sounds Based on a Sinusoidal Representation , 1987, ICMC.

[2]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[3]  Xiaoming Lai,et al.  Interpolation methods for time-delay estimation using cross-correlation method for blood velocity measurement , 1999, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[4]  Nafiz Arica,et al.  An overview of character recognition focused on off-line handwriting , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[5]  Mark A. Richards,et al.  Fundamentals of Radar Signal Processing , 2005 .

[6]  Damien Garcia,et al.  Robust smoothing of gridded data in one and higher dimensions with missing values , 2010, Comput. Stat. Data Anal..

[7]  Romit Roy Choudhury,et al.  Using mobile phones to write in air , 2011, MobiSys '11.

[8]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[9]  Toby Sharp,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR.

[10]  Tanja Schultz,et al.  Vision-based handwriting recognition for unrestricted text input in mid-air , 2012, ICMI '12.

[11]  Dina Katabi,et al.  RF-IDraw: virtual touch screen in the air using RF signals , 2014, S3 '14.

[12]  Dina Katabi,et al.  RF-IDraw: virtual touch screen in the air using RF signals , 2014, S3@MobiCom.

[13]  Xinyu Zhang,et al.  mTrack: High-Precision Passive Tracking Using Millimeter Wave Radios , 2015, MobiCom.

[14]  Ivan Poupyrev,et al.  Soli , 2016, ACM Trans. Graph..

[15]  Linas Svilainis,et al.  Analysis of the interpolation techniques for time-of-flight estimation , 2016 .

[16]  Chen Chen,et al.  The Promise of Radio Analytics: A Future Paradigm of Wireless Positioning, Tracking, and Sensing , 2018, IEEE Signal Processing Magazine.

[17]  K. J. Ray Liu,et al.  Wireless AI: Wireless Sensing, Positioning, IoT, and Communications , 2019 .