Domain adaptation for handwriting trajectory reconstruction from IMU sensors

2026-07-29

AI Summary

This study addresses the challenge of limited cross-population generalization in IMU-based handwriting trajectory reconstruction, caused by distributional discrepancies in inertial signals between adults and children due to differences in writing speed and confidence. To bridge this gap, the work introduces domain adaptation into the task for the first time, learning a unified intermediate feature representation that effectively aligns signal distributions across user groups. Experimental results demonstrate that the proposed approach significantly outperforms both training from scratch and fine-tuning strategies, achieving more robust and accurate trajectory reconstruction in cross-domain scenarios. This advancement offers a promising new direction toward developing handwriting sensing systems applicable across diverse populations.

Abstract

Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation. This study focuses on a digital pen equipped with kinematic sensors, allowing users to write on any surface while simultaneously preserving a digital trajectory of handwriting. This technology holds significant potential as a valuable educational tool, particularly in classrooms where it can facilitate the process of learning to write. A major issue is based on the difference in captured signals between adults and children. For similar handwriting trace, we have large differences in sensor signals due to differences in speed and confidence in the handwriting gesture of children. To address this, we investigate a domain adaptation approach to build a unified intermediate feature representation aimed at facilitating the trajectory reconstruction. We demonstrate the interest of domain adaptation methods in leveraging existing knowledge for application in different contexts. Specifically, we compare our domain adaptation approach with two other methods: training the model from scratch and fine-tuning the model.

Problem

• domain adaptation
• handwriting trajectory reconstruction
• IMU sensors
• digital pen
• signal discrepancy

Innovation

• domain adaptation
• handwriting trajectory reconstruction
• IMU sensors
• digital pen
• feature representation