发表机构
IRISA; Universite de Rennes; INSA Rennes; Universite Rennes 2（IRISA（法国国家信息与自动化研究所联合研究中心）; 雷恩大学; 雷恩国立应用科学学院; 雷恩第二大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对成人与儿童手写传感器信号差异问题，研究提出领域适应方法构建统一特征表示，对比从零训练、微调模型，以实现IMU传感器的手写轨迹重建。
AI 中文摘要
数字笔常用于在数字设备上书写，可提供手写轨迹并增强人机交互。本研究聚焦于配备运动传感器的数字笔，用户可在任意表面书写，同时保留手写数字轨迹，该技术作为有价值的教育工具具有重大潜力，尤其在课堂中可促进书写学习过程。主要问题在于成人与儿童采集的信号存在差异：对于相似的手写轨迹，因儿童书写手势的速度和熟练度不同，传感器信号差异显著。为解决此问题，本研究探究领域适应方法以构建统一的中间特征表示，助力轨迹重建。研究证明领域适应方法可利用现有知识应用于不同场景，具体将所提领域适应方法与另外两种方法对比：从零训练模型和微调模型。
英文摘要
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.
Journal refDocument Analysis and Recognition, ICDAR 2024 Workshops
DOI：10.1007/978-3-031-70645-5_1
