基于共享表示和领域特定头的IMU数字笔年龄自适应手写重建
Age-Adaptive Handwriting Reconstruction from an IMU-Based Digital Pen through Shared Representations and Domain-Specific Heads
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中文总结 AI 辅助
针对IMU数字笔手写重建中成人与儿童信号差异问题，提出基于时间卷积网络和多个预测头的跨领域学习架构，利用共享特征实现跨年龄组鲁棒重建。
中文摘要 AI 辅助
数字笔被广泛用于在数字设备上捕获手写内容，实现精确的轨迹记录并增强人机交互。然而，大多数数字笔与平板电脑捆绑销售，缺乏跨品牌兼容性。近年来，配备运动传感器的数字笔已经出现，可在任何表面上使用。这尤其为支持课堂中的手写采集开辟了巨大潜力。由于成人和儿童之间传感器信号的显著差异，从这种配备IMU的笔进行手写重建构成了一项挑战。即使产生视觉上相似的轨迹，书写动态、运动控制、握笔方式和用户信心的差异也会导致捕获信号中的显著差异。此外，儿童手写的高度变异性要求收集大量数据，而这在学校环境中大规模实施是不可行的。此外，仅用成人数据训练的模型无法泛化到儿童手写，反之，用儿童数据训练的模型在成人书写者上表现不佳。这种跨人群退化凸显了对统一模型的需求，该模型可直接部署在笔上，无需任何用户特定适配。为解决此问题，我们提出了一种跨领域学习方法，使用基于时间卷积网络和多个预测头的原始神经网络架构。该模型旨在通过利用共享特征来对跨年龄组具有鲁棒性，同时有效处理由图形运动发育差异引起的变异性。此方法旨在改进从传感器数据重建手写轨迹，其中每个领域都受益于另一领域提供的额外数据。
英文摘要
Digital pens are widely used to capture handwriting on digital devices, enabling precise trace recording and enhancing human-computer interaction. However, most are bundled with tablets and lack cross-brand compatibility. Recent digital pens equipped with kinematic sensors have emerged, designed for use on any surface. This especially opens significant potential for supporting handwriting acquisition in classrooms. Handwriting reconstruction from such an IMU-equipped pen poses a challenge due to the significant variability in sensor signals between adults and children. Even when producing visually similar traces, variations in writing dynamics, motor control, pen holding, and user confidence introduce substantial discrepancies in the captured signals. Additionally, the high variability in children's handwriting requires collecting large amounts of data, which is not feasible to implement in a school environment at scale. Furthermore, models trained exclusively on adult data fail to generalize to children's handwriting, and conversely, models trained on children's data perform poorly on adult writers. This crosspopulation degradation highlights the need for a unified model that can be deployed directly on the pen, without any user-specific adaptation. To address this issue, we propose a cross-domain learning, using an original neural network architecture based on a Temporal Convolutional Network and multiple prediction heads. The model is designed to be robust across age groups by leveraging shared features while effectively handling variability induced by differences in graphomotor development. This approach aims to improve handwriting trace reconstruction from sensor data, where each domain benefits from additional data provided by the other domain.
发表机构
- Luleå University of Technology（吕勒奥理工大学）
- Univ Rennes（雷恩大学）
- CNRS（法国国家科学研究中心）
- IRISA（法国雷恩信息与自动化系统研究所）
机构由 AI 辅助整理，请以论文原文为准。
