3D Handwriting Trajectory Recovery by Wireless Sensing with Commercial LTE Signals

Integrated sensing and communication (ISAC) is an essential technology in next-generation wireless networks. Leveraging cellular signals for device-free wireless sensing and handwriting trajectory recovery has enormous potential in various human-machine interaction applications. However, wireless channel characteristics vary with the position and orientation of the user, as well as the locations of transceivers, leading to a great challenge for wireless sensing algorithms. This paper introduces a position and orientation independent handwriting trajectory recovery scheme based on multiple wireless links. By using a preamble gesture to extract environmental information, we can convert the Doppler frequency shifts of different transmitterreceiver pairs to 3D velocities in the body coordinate system and recover handwriting trajectories. We build a prototype system and use commercial 4G-LTE signals to extract channel state information without inter-cell interference. Real-time handwriting trajectory recovery is achieved in a practical environment, demonstrating the effectiveness of the proposed method.