MilliBack

Abstract

Real-time handwriting tracking is important for many emerging applications such as Artificial Intelligence assisted education and healthcare. Existing movement tracking systems, including those based on vision, ultrasound or wireless technologies, fail to offer high tracking accuracy, no learning/training/calibration process, low tracking latency, low cost and easy to deploy at the same time. In this work, we design and evaluate a wireless backscattering based handwriting tracking system, called MilliBack, that satisfies all these requirements. At the heart of MilliBack are two Phase Differential Iterative (PDI) schemes that can infer the position of the backscatter tag (which is attached to a writing tool) from the change in the signal phase. By adopting carefully-designed differential techniques in an iterative manner, we can take the diversity of devices out of the equation. The resulting position calculation has a linear complexity with the number of samples, ensuring fast and accurate tracking. We have put together a MilliBack prototype and conducted comprehensive experiments. We show that our system can track various handwriting traces accurately, in some testings it achieve a median error of 4.9 mm. We can accurately track and reconstruct arbitrary writing/drawing trajectories such as equations, Chinese characters or just random shapes. We also show that MilliBack can support relatively high writing speed and smoothly adapt to the changes of working environment.