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This study investigates the application of domain adaptation techniques to improve handwriting trajectory reconstruction from IMU sensor data in digital pens, addressing the significant signal discrepancies between adult and child handwriting. By creating a unified intermediate feature representation, the researchers demonstrate that domain adaptation can effectively leverage existing knowledge to enhance performance in varied contexts. The results show that this approach outperforms both training from scratch and fine-tuning methods, highlighting its potential for educational applications in handwriting learning.
Domain adaptation can bridge the gap in handwriting signal discrepancies between adults and children, leading to more effective trajectory reconstruction.
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.
