发表机构
Ontario Tech University; Microsoft Research（安大略理工大学; 微软研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出Magic Pen技术，基于LSTM模型结合迁移学习实现数字笔模式自动切换，经多组用户实验验证，其交互体验优于传统菜单式方法，可降低交互成本与模式错误。
AI 中文摘要
传统数字笔界面使用菜单按钮切换笔模式，这会导致往返交互耗费时间和认知负荷，还会因点击小型模式选择按钮产生模式错误。本研究提出了Magic Pen（魔法笔）技术，该技术利用机器学习自动切换数字笔模式，无需显式更改模式。Magic Pen由LSTM模型驱动，该模型基于两项研究中27名参与者收集的笔数据进行训练，并采用迁移学习迭代调整模型以适配特定用户的标注习惯。系统整合了轻拂手势或屏幕点击的错误缓解技术，用于快速纠正模式错误或删除笔画。我们对18名参与者开展了与传统菜单式方法的对比研究，随后进行迭代改进，再对8名参与者开展部署研究。结果显示，与传统菜单式方法相比，Magic Pen更受青睐，且迁移学习提升了模型的可预测性和稳定性。
英文摘要
Traditional digital pen interfaces use menu buttons to change the pen mode, which results in time and cognitive load spent on round-trip interactions and mode errors from tapping small mode selection buttons. This work presents the Magic Pen, a technique which uses machine learning to automatically switch between digital pen modes without requiring explicit mode changes. Magic Pen is driven by an LSTM model trained on pen data collected from 27 participants across two studies and uses transfer learning to iteratively tune the model towards how a specific user annotates. Error mitigation techniques using a flick gesture or on-screen tap are incorporated to correct mode errors or remove a stroke quickly. We evaluated Magic Pen in a comparative study with 18 participants, followed by iterative improvements and a deployment study with 8 participants. Magic Pen was preferred compared to a conventional menu-based approach, and transfer learning allowed for greater model predictability and stability.
CommentsTechnical report. 18 pages, 18 figures, 3 tables
